Models of Causality in Psychopathology: Toward Dynamic, Synthetic, and Nonlinear Models of Behavior Disorders 0023528311, 9780023528316

Citation preview

Models of Causality in Psychopathology

General Psychology Series

Digitized by the Internet Archive In 2023 with funding from No Sponsor

https://archive.org/details/modelsofcausalit0000hayn

K

GENERAL PSYCHOLOGY SERIES EDITORS Arnold P. Goldstein, Syracuse University Leonard Krasner, Stanford University & SUNY at Stony Brook

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY (PGPS-168)

Titles of Related Interest Hersen/Kazdin/Bellack THE CLINICAL PSYCHOLOGY HANDBOOK,

Second Edition

Hersen/Last HANDBOOK OF CHILD AND ADULT PSYCHOPATHOLOGY: A Longitudinal Perspective Kazdin RESEARCH DESIGN IN CLINICAL PSYCHOLOGY, Second Edition Schwartz/Johnson PSYCHOPATHOLOGY OF CHILDHOOD: A Clinical-Experimental Approach, Second Edition

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY Toward Dynamic, Synthetic and Nonlinear Models of Behavior Disorders

STEPHEN N. HAYNES University of Hawaii

Macmillan

Publishing Company New

Maxwell ie Ney

York

A

oa Canada

cues Oxford Singa

ieornaa

Copyright © 1992 by Macmillan Publishing Company, a division of Macmillan, Inc. Printed in the United States of America

All rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the Publisher. Macmillan Publishing Company is part of the Maxwell Communication

Group of Companies. Maxwell! Macmillan Canada, Inc.

1200 Eglinton Avenue East Suite 200 Don Mills, Ontario M3C 3N1

Library of Congress Cataloging-in-Publication Data

Haynes, Stephen N. Models of causality in psychopathology : toward dynamic, synthetic, and nonlinear causal models of behavior disorders / by Stephen N. Haynes. p. cm.—(General psychology series ; 168) Includes bibliographical references and indexes. ISBN 0-02-352831-1 (hardcover : alk. paper) 1. Mental illness—Etiology. 2. Psychology, Pathological. |. Title. Il. Series. [DNLM: 1. Mental Disorders—psychology. 2. Models, Psychological. 3. Psychopathology. WM 100 H424m] RC454.4.H39 1991 616.89'071—dc20 DNLM/DLC for Library of Congress 90-14366 CIP Printing: 123456789 Year:23456789 The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences—Permanence of Paper for Printed Library Materials,

ANSI Z39.48-1984

CONTENTS

Introduction Acknowledgments

xt XVIL

SECTION I: INTRODUCTION TO CONCEPTS OF CAUSALITY AND CAUSAL MODELS OF BEHAVIOR DISORDERS Chapter 1: Introduction to Causal Models of Behavior Disorders Construct Systems and Models of Causality The Impact of Models of Causality of Behavior Disorders On Clinical Interventions On Community Interventions On the Methods and Focus of Assessment On Prevention Strategies On Research Foci and Methods Interventions Independent of Causal Models Summary The Parameters of Behavior Disorders Alternative Methods of Communicating About Causal Relationships: Causal Path Diagrams and Functional Equations Causal Path Diagrams Functional Equations Methods of Modifying Causal Influence Reducing Exposure to a Causal Variable Directly Affecting the Strength of a Causal Relationship Indirectly Affecting the Strength of a Causal Relationship Modifying the Mechanism of Causal Action Summary The Evolution of Synthetic and Dynamic Causal Models Parsimony of Causal Models Conclusion Chapter 2: Concepts of Causality and Causal Relationships

Basic Concepts of Causality Divergent Concepts of Causal Relationships Vv

5) pds)

vi

CONTENTS

Domain and Level of Causal Relationships Drawing Causal Inferences Causal Mechanisms or Paths An Example Summary Functional and Causal Relationships Summary The Imperfect Determination of Behavior Disorders: The Probabilistic Nature of Causal Relationships Necessary Conditions for Causal Relationships Covariation Temporal Precedence of the Causal Variable The Exclusion of Alternative Explanations for the Relationship A Logical Connection Between Variables Additional Characteristics of Causal Relationships Causal Relationships Are Probabilistic Causal Relationships Are Nonexclusive Causal Relationships Can Be Bidirectional Causal Relationships and Models Are Always Conditional Necessary and Sufficient Causal Variables Causal Relationships and Models Are Dynamic Causal Variables Vary in Level Causal Variables Can Be Modifiable or Unmodifiable Summary Causal Relationships and Causal Models Of Behavior Disorders Causal Models Emphasize Important Modifiable Causes Additional Components and Characteristics of Causal Models of Behavior Disorders Values in Causal Inference Conclusion Chapter 3: Limited Causal Models Characteristics of Limited Causal Models Univariate Causal Models A Presumed Incompatibility Between Causal Models Unspecified Causal Mechanisms Unacknowledged Domains Excessively High Level Linearity Unidirectionality A Presumed Stability or Finality Confusing Noncausal with Causal Functional Relationships Descriptive and Explanatory Variables

CONTENTS

Vil

The Effects of Limited Causal Models The Maintenance of Limited Causal Models Conclusion

67 68 71

Chapter 4: Classes of Causal Variables and the Parameters of Behavior Disorders Classes of Causal Variables The Parameters of Behavior Disorders and The Specificity of Causal Effects The Parameters of Behavior Disorders Reconsidered Original Causes Maintaining Causes Triggering Causes Risk Factors and Marker Variables Causal Variables That Affect the Magnitude or Duration of a Behavior Disorder Causal Variables That Affect the Topography of a Behavior Disorder The Diversity of Causal Variables or Conditions for Behavior Problems Causal Variables That Affect Posttreatment Relapse Summary and Implications Latent Causal Variables Conclusion

72 Te.

Be, 73 76 80 81 82 83 84 85 86 87 89 91

SECTION II: MULTIPLE CAUSALITY AND THE RELATIONSHIPS AMONG CAUSAL VARIABLES Chapter 5: Multiple and Idiosyncratic Causal Variables in Behavior Disorders

Multiple Causal Variables for Behavior Disorders Examples Issues Implications Summary Multiple Causal Paths Examples Implications Summary Individual Differences in the Strength of Causal Relationships Implications Conclusion

95 95 96 98 101 102 103 103 104 107 108 109 111

Vill

CONTENTS

Chapter 6: Interactive Causal Relationships Interactive Causality Additional Examples of Interactive Capea Models Complex Forms of Interactive Causal Relationships Mechanisms of Interactive Causality The Strength of Interactive Causal Relationships Interactive and Additive Causal Relationships Mediating and Moderating Variables Clinical Implications Identifying Interactive Causal Relationships Interactive Causality and Vulnerability to Behavior Disorders A Definition of Vulnerability Vulnerability and the Diathesis-Stress Models of Psychopathology Vulnerability and Latent and Original Causal Variables Clinical and Empirical Utility Conclusion SECTION III: DYNAMIC, NONLINEAR, AND DISCONTINOUS CAUSAL RELATIONSHIPS Chapter 7: The Time Course of Causal Relationships Causal Latency Examples of Long Causal Latencies Research and Clinical Implications Mediation and Mechanisms of Causal Latency Equilibrium States, Equilibrium Latency, and Transitional Periods Research and Clinical Implications Additional Issues Summary Chronicity The Chronicity of Causal Variables The Chronicity of Behavior Disorders The Time Cluster of Causal Variables Conclusion Chapter 8: State-Phase Functions in Causal Relationships

State-Phase Functions Implications for Causal Inference Research and Clinical Implications Additional Issues Summary

CONTENTS

Change and Contrast Effects as Causal Variables Examples

Research and Clinical Implications Additional Issues Summary Conclusion Chapter 9: Nonlinear and Discontinuous Causal Relationships

Nonlinear Causal Relationships Discussion Summary Causal Discontinuity: I. Critical Levels in Causal Relationships Causal Discontinuity Critical Levels in Causal Relationships Examples Summary and Discussion Causal Discontinuity: II. Functional Plateaus Examples Discussion Summary Causal Discontinuity: II]. Temporal Dependence and Sensitive Periods in Causal Relationships Temporal Dependence in Causal Relationships Sensitive Periods in Causal Relationships Nonlinearity and Causal Discontinuity: General Discussion and Research and Clinical Implications General Discussion of Nonlinearity and Causal Discontinuity Implications for Research and Data Analysis Clinical Implications Conclusion Endnotes Glossary References Author Index Subject Index

About the Author Series List

.

y

‘

a's]

i

j

i

1 Weg

a

; ae

p-h”

-

“SA

pris.

yy

; ee

Se ,

eceadier Tneral Ae Noe

Wt) co yaad te

ae a)

brn +

x

whiny ng ee Beadan

SVT

iw

5

. fi

ener

a

4

afr

Sate

BIT

'

eg onal ve 1 WP

Vile Mulroy Pee ieee L

See

/

@Roihecgal (a

‘iegi ed Teaco Bs

|

“

Chapter 5

MULTIPLE AND IDIOSYNCRATIC CAUSAL VARIABLES IN BEHAVIOR DISORDERS

Chapter 4 examined the parameters of behavior disorders and how they are differentially affected by causal variables. This chapter examines another aspect of synthetic causal models of behavior disorders: How a behavior disorder is often affected by multiple causal variables. The initial sections of this chapter consider the role of multiple causal variables. Subsequent sections consider multiple causal paths and individual differences in the impact of causal variables. Most behavioral scientist-practitioners acknowledge the multivariate and idiosyncratic nature of behavior disorder causality. Consequently, this chapter will focus primarily on examining the clinical and empirical implications of these concepts.

MULTIPLE CAUSAL VARIABLES FOR BEHAVIOR DISORDERS The idea of multiple causality of disorders is not new (see Boring, 1957; Descartes, 1724; Pavlov, 1928), and behavior disorders are increasingly acknowledged to be a function of multiple causal variables (Kanfer, 1985; Bandura,

1981; Haynes & O’Brien,

1988; Ollendick

& Hersen,

1984). Neverthe-

less, univariate causal models have dominated, until recently, the research and treatment models in psychopathology. The concept of multivariate causality incorporates two ideas: (1) There are often multiple possible causes

of a behavior disorder,

and (2) there are often

multiple causes operating for an individual with a specific behavior disorder. DS

96

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

Individual differences in the array of operational causal variables for a behavior disorder will be considered later in this chapter. A multivariate causal model for one client whose main complaint was sleeping difficulties is illustrated in Figure 5.1 (Haynes & O’Brien, 1990). It can be noted that several causal variables are hypothesized to influence this client’s sleeping difficulties (in this case, ‘‘sleeping disorder’’ refers to three parameters—sleep onset, sleep maintenance, early morning awakenings). Other aspects of the causal model are also notable: (1) multiple causal variables, (2) multiple causal paths between variables (between X. and Y>), (3) correlated, noncausal relationships (between Y, and Y>), (4) bidirectional causal relationships (between Y> and Xg), (5) effects of an identified behavior problem (Y> and Y3), (6) varying strengths of causal relationships, and (7) unidentified causal variables (R>).

Examples Many recent causal models of behavior disorders have included multiple causal variables. For example, male and female sexual dysfunctions (e.g., erectile failure, dyspareunia) have been hypothesized to be a function of multiple variables such as diabetes, hormonal

dysfunctions, attentional processes,

vascular

impairment, early learning, environmental contexts, fatigue, relationship distress, social-sexual

interactions,

and conditioned

fear reactions (Friedman

&

Hogan, 1985; Kaplan, 1979; LoPiccolo, 1980). Causal models of learning disorders of children have included various permutations of linguistic problems, visual-perceptual disorders, genetic-biochemical factors, diet, organic brain dysfunctions, attentional deficits, situational factors, and response contingencies (Cantwell,

1986; Ross & Ross,

1982).

Schizophrenia has also been the object of multivariate causal models. Causal models of this broad class of disorders have included neurotransmitter dysfunctions, genetic factors, prenatal and perinatal trauma, organic brain dysfunctions, attention deficits, environmental

stressors, and family interactions (Kar-

son et al., 1986; Mirsky & Duncan, 1986). Causal models of substance abuse have proposed important causal roles for environmental stressors, mood, anxiety, genetic susceptibility, early experience with the substance, motivation, coping skills, exposure to substance cues, out-

come expectations, recent history of substance use, social facilitation and modeling, self-efficacy, conditioning, affect enhancement properties of the substance, neurotransmitter receptor sensitivity and aversion relief (Brownell, Martlatt, Kichtenstein, & Wilson, 1986; Donovan & Chaney, 1985; Litman, 1980; Marlatt, 1985; Marlatt, Baer, Donovan, & Kivlahan, 1988; McCrady, 1985; Zucker, 1987; Zucker & Gomberg, 1986).

MULTIPLE AND IDIOSYNCRATIC CAUSAL VARIABLES

7,

Residual

R , Causa

Factor 1

1

er

Distrust

x 4

f X,

Residual

R 5 Causal

Outcome Expectation

itive Work Interactions

Factor 2

Sleep ers

Y;

2

Barbiturate Intake

X, oe

Internal

Attribution

Presleep

X Alcohol Intake

Negative Rumination

X = Causal Variable Y = Dependent Variable R = Residual Causes

Strong Relationship

——

Weak Relationship

+? ___ Bidirectional Causal Relationship a >

Aerobic

»&

nme

Exercise

P = Negative Relationship

Covarying Non-causal

Relationship

Figure 5.1 Causal vector diagram for a case involving depression, insomnia, and substance

abuse. The diagram demonstrates causal, noncausal, and reciprocal causal relationships and various strengths of causal relationships.

Multivariate causal models have been proposed for other behavior problems such as visual impairment (Lundervold, Levin, & Irvin, 1987), depression (Nezu, 1987), irritable bowel syndrome (Latimer, 1983; Sammons & Karoly, 1987), paranoia (Haynes, 1986b), tension headache (Haynes, 1981), adolescent antisocial behavior Harris,

(Patterson,

1986),

speech disorders

1977), attention-deficit disorders (Cantwell,

(Goren,

Romanaczyk,

&

1986), energy waste (Cos-

tanzo, Archer, Aronson, & Pettigrew, 1986), adolescent deviant behavior (Jessor & Jessor, 1977), obesity (Keesey & Powley, 1986), blood-glucose levels and diabetes (Brownlee-Duffeck et al., 1987), hypertension (Kaplan, 1982), and child abuse (Wolf,

1985).

In summary, many causal models proposed for behavior disorders exemplify the synthetic qualities advocated in this book: They have invoked an extensive array of cognitive, psychological, behavioral, social-environmental, and physiological variable classes. It is particularly important to note that dissimilar classes of causal variables have been proposed to result in similar behavior problems.

98

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

Issues

Level of Variables and Mechanisms The degree to which a causal model of a behavior disorder invokes multiple determinants is influenced by the level of the causal variables. As Bunge (1963) noted, several statistically or conceptually related lower-level causal variables are sometimes treated as a single higher-level causal variable; he labeled this conjunctive multiple causation. Many of the causal variables invoked in causal models of behavior disorders are higher-level variables. For example, the construct ‘‘life stressors’? has been included in causal models of depression, psychophysiological disorders, schizophrenia, substance abuse, and many other behavior disorders (e.g., Haynes & Gannon, 1981; Davison & Neale, 1990; Kaplan, 1983; Mathews et al., 1986). ‘“Life stressors’’ is a higher-level con-

struct in that it is composed of many lower-level variables (e.g., marital difficulties, health problems) that are considered

to act in an additive fashion to

affect the parameters of behavior disorders. Other higher-level causal constructs 99 66 include ‘‘neurotransmitter dysfunctions,’’ ‘‘social skills deficits, expressed emotion,’’ ‘‘social anxiety,’’ “‘social support,’’ ‘‘irrational beliefs,’’ and ‘‘negative self-concept.’’ Each of these higher-level variables is often included in causal models of behavior disorders to facilitate communication among behav99

66

ioral scientist-practitioners. In these cases, it is presumed that Y = X,, but X, = f(X2 + X; +... + X,). In reference to the example given, X; would be the ‘‘life stressors’’ variable, while X>, . . . , X, would be individual life stressors.

Many assessment instruments (e.g., “‘life stressor’ questionnaires, observed rates of “‘positive interactions’? among family members, ‘‘autonomic lability’ scores) give a summary “‘score’’ for a person on a higher-level causal variable. This coefficient is derived by adding up responses to a number of lower-level components. Consequently, coefficients of a higher-level variable cannot be used to infer values of its lower-level components (e.g., a “‘life stress’’ score cannot be used to indicate the level of stress in family relationships). Thus, the number of causal variables in a causal model is somewhat arbitrary and influenced by the level of the constructs invoked. As discussed previously, the best level of a causal variable is that which optimally facilitates clinical and empirical decision making; lower-level variables are more often better suited for this purpose than are higher-level variables.

Mechanisms of Multiple Causality As with previous concepts of causality, important issues arise when we try to explain a phenomenon.

How

(or in what way) can a behavior disorder be

influenced by multiple causes? How can such apparently dissimilar variables have similar effects? For example, how can both caffeine and life stressors cause migraine headaches? These questions again direct us to an analysis of the

MULTIPLE AND IDIOSYNCRATIC CAUSAL VARIABLES

Se)

mechanisms of the causal relationships. In effect, many apparently disparate causal variables operate through common causal mechanisms and, therefore, have similar effects (this is referred to as a final common pathway by some theorists). Let us limit our causal analysis of child abuse to one possible path—socialenvironmental stressors acting on the parent. In this case, the role of a common path for multiple causal variables becomes clear. Because a ‘‘stressful environment’ is a proximal triggering causal variable for child abuse, any stressful event for the parent can function as a causal variable. Stressful events triggering abusive behavior may include a noncompliant child, a marital disagreement, or withdrawal from a psychoactive substance. These different causal variables have similar effects because they operate through a common causal mechanism. Migraine headache provides another example of how multiple causal variables can operate through a common causal mechanism. If migraine headache results from dilation of the cranial arteries, any variable affecting cerebrovascular dilation can trigger migraine headache. Consequently, migraine headaches would be expected to occur in poststress recovery periods (because of decreased levels of circulating catecholamines, which are vasoconstrictors), during phases of the menstrual cycle associated with decreased levels of circulating estrogen (because estrogen is a powerful vasoconstrictor), and from the intake of certain wines and processed foods (because they sometimes contain tyramine, a powerful vasoactive substance) (Adams,

Feverstein, & Fowler,

1980;

Anthony, 1988; Blau & Diamond, 1985; Levor, Cohen, Naliboff, McArthur, & Heuser, 1986). The number of potential causal variables for a behavior disorder is further increased because many behavior disorders can be affected through multiple causal paths. For example, decreased inhibitions for emitting socially condemned behavior may constitute a separate causal path for child abuse. This, in turn, may be affected by the intake of ethyl alcohol or other substances that modify expectancies or perceptions, being alone with the child, or a social context in which physical aggression toward children is condoned. For example, hypertension may result from renal dysfunctions, fluid retention, obesity, autonomic nervous system hyperreactivity, baroreceptor dysfunction, and increased peripheral vascular resistance, (Anderson et al., 1986; Genest, Kuchel, Hamet, & Cantin,

1983; Gross & Strasser,

1983; Kaplan,

1982;

Mathews et al., 1986). Furthermore, each mechanism may be affected by multiple behavioral, cognitive, and physiological variables.

Parsimony Versus Comprehensiveness of Models As noted in previous chapters, the emphasis on multiple causal variables and mechanisms does not negate the possibility that a single causal variable may account for the variance in a behavior problem. As Baer (1984, 1986) sug-

100

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

gested, a rigid imposition of a multivariate causal model can mask the operation of a single principal causal variable by assigning important causal power to unimportant causal variables. Priestly (1988), in discussing nonlinear timeseries analysis, advocated a balance between the complexity of a predictive model (the number of predictor variables) and its predictive efficacy. In some cases, the addition of more causal variables is not warranted by the increased explanatory power of the model. In some causal models, the strengths of individual causal relationships may be so disparate that some variables can be removed without significantly reducing the power or utility of the model (assuming equal variance among the variables; Bunge,

1963; McCormick,

1937). This would result in a more parsimo-

nious model with greater clinical utility. As noted in chapter 3, however, causal models that emphasize the primacy of a single variable often have limited validity and utility. A ‘‘weak’’ causal variable is also not necessarily unimportant. The importance of a causal variable depends not only on its shared variance with the behavior problem but also on the relative strength of other causal variables (there may not be a ‘‘principal’’ causal variable) and on the personal and social significance of the targeted behavior disorder. A variable that controls only 10% of the variance in domestic violence may be worthy of empirical and clinical attention because of the personal harm associated with physical abuse. Alternatively, a variable that controls 10% of the variance in mild tension headache may not warrant significant study if more important variables were iden-

tified. A balance between economy and comprehensiveness in causal models is difficult to maintain (Hyland, 1981). While a multivariate causal model can often

provide a more accurate representation of the determinants of a behavior disorder, excessively elaborate models can impede empirical and clinical decisionmaking abilities when they diminish the impact of some important variables. Thus, it is possible for synthetic causal models to become conceptually iatrogenic—they can impede the decision-making processes they are intended to facilitate. It is also possible for the operation of one causal variable to mask the operation of another. Beutler and Gleason (1981), for example, noted that both

organic and psychological factors may be influential in a case of sexual dysfunction, but the causal impact of one may mask the impact of the each other. In a case of male erectile dysfunction, the absence of nocturnal penile tumnescence may suggest the operation of organic factors but does not speak to the validity of psychological causal variables operating in conjunction with the organic one. Confirming organic involvement may lessen the chance that important psychological factors will be investigated. The chance of masking is reduced if the nonexclusive nature of causal relationships is recognized; the

MULTIPLE AND IDIOSYNCRATIC CAUSAL VARIABLES

10]

identification of an important causal relationship should not terminate the search for others. Despite the problems associated with multivariate causal models, the more common error associated with causal models of behavior disorders has been the advancement of inappropriately univariate, rather than inappropriately multivariate, models. Although behavioral scientists-practitioners should retain an openness to the possible appropriateness of limited causal models (see chapter 3), a presumption of multivariate causal variables will very often lead to a more powerful explanatory model of a behavior disorder.

Implications The operation of multiple causal variables amplifies the probabilistic nature of causal models. Each variable contributes unique sources of imprecision, and the independent, additive, and interactive effects of multiple causal variables cannot be exactly specified. The addition of variables to a causal model also increases the chance that some will not be adequately measurable (Levins, 1974). Furthermore, each variable may, in turn, be affected by several other imprecisely specified causal variables. Consequently, the predictive accuracy of multivariate causal models will always be imperfect. As Simon (1977) noted, the value of a particular variable provides, at best,

only a range of possible effects. Although the “‘average’’ strength of relationship between two variables should be stable across repeated assessments with large samples, the exact relationship between two variables for a single case can never be determined because the effects of other concurrently operating variables can never be exactly specified, and measurement error is unavoidable. As stressed in chapter 2, imperfection of a causal model does not imply a lack of utility. Despite inherent measurement imprecision and random and nonrandom variation in causal events, the identification and measurement of multiple variables within synthetic causal models of behavior disorders will lead to enhanced predictive power and clinical decision making. As illustrated in Figure 5.1, the effects of a causal variable can also be affected by its interactions with other causal variables. Causal variables can exhibit additive and interactive relationships, in addition to their independent effects on behavior disorders. Consequently, the relationships among causal variables have an important influence on which variables are targeted for intervention efforts. Interactive causal relationships are addressed in chapter 6. An emphasis on multivariate causal models also promotes broadly focused preintervention assessment (1.e., broad-spectrum assessment; Ciminero, 1986; Conger & Keane, 1981; Hawkins, 1986; Haynes, 1978, 1989, 1990; Hersen, 1981; Nelson & Hayes, 1986). For example, children’s social isolation (TofteTipps, Mendonca, & Peach, 1982) may result from varying permutations of

102

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

multiple determinants, including inadequate parental modeling of social behaviors, insufficient exposure to social learning experiences, reinforcement of inappropriate social-interaction behaviors, interference with social behaviors by elevated “‘anxiety’’ responses (conditioned emotional responses), expectancies, and/or punishment of social interactions (Bijou & Baer, 1978; Maccoby & Martin, 1983). Consequently, broadly focused, multimodal, preintervention assessment is necessary to identify which of these potential causal factors are influential for a particular child (Evans & Nelson, 1988; Ollendick & Hersen, 1984).

1977; Haynes,

1990; Mash & Terdall,

Multivariate causality also promotes multiply focused intervention strategies. Although specific intervention foci will be influenced by the relative importance of, and interactions among, causal variables, a focus on more than one important causal variable would be expected to enhance intervention outcome. This is because an intervention focus on multiple causal variables will more likely target a greater proportion of the controlling variance in the behavior disorder. Rather than limiting treatment of a child’s social isolation to reducing conditioned anxiety (e.g., through gradual exposure to social situations), treatment effectiveness may be enhanced by also focusing on parental prompts and the child’s self-instructions, expectancies, and labeling. For the same reasons, multivariate assessment will be necessary to evaluate the effects of intervention comprehensively.

Summary Behavior disorders are increasingly considered to be a function of multiple rather than single causal variables. The multivariate nature of causal models is partially a function of the level of the causal variables. An examination of causal mechanisms provides the best approach to understanding multivariate causal relationships. Despite an emphasis on multiple causal factors in synthetic causal models, the possibility that a single causal factor may substantially account for a person’s behavior disorder must be acknowledged. However, conceptual errors in explaining behavior disorders more often involve an unwarranted emphasis on single, rather than on multiple, causal factors. Multiple causal variables present conceptual and measurement problems in that one causal variable can mask the operation of another and synthetic causal models can sometimes become unnecessarily elaborate. There are several implications associated with an emphasis on multivariate causal models of behavior disorders. First, the operation of multiple causal variables accents the probabilistic nature of causal relationships. Multivariate causal models also promote an emphasis on multifocused and multimethod preintervention assessment. The interactions among multiple causal variables

MULTIPLE AND IDIOSYNCRATIC CAUSAL VARIABLES

103

Suggest the utility of a multifocused treatment strategy and have an important influence on which variables are targeted for intervention.

MULTIPLE CAUSAL PATHS A corollary of multivariate causality for behavior disorders is the operation of multiple causal paths (see chapter 1).* That is, there are often a number of mechanisms through which a causal relationship between two variables can occur. The operation of multiple causal paths is particularly evident among higher-level causal variables such as ‘‘stress,’’ ‘‘dietary factors,’’ ‘early trauma,”’ and *‘social contingencies.”’ A single, lower-level variable can also exert causal influence through multiple differentially weighted, concurrently operational, paths. These are illustrated in Figure 5.2.

Examples Examples of multiple causal paths are commonly found in causal models of behavior disorders. They are particularly prevalent in models that examine the effect of life stressors on behavior disorders. For example, there is some sup-

port for a relationship between chronic life stressors and impaired immune system functioning (Asterita, 1985). Jemmott and Locke (1984) suggested that life stressors may affect immune

system functioning (and, therefore, susceptibility

to infectious diseases) through increased drug use, and changes in diet, and sleep disruption. Barnett and Gotlib (1988) proposed several mechanisms to account for the relationship between social isolation and depression. These included heightened interpersonal dependency (e.g., relying solely on the positive regard of a single person for self-esteem), a lack of social support to buffer negative life events, a failure to develop secondary roles or broader sources of reinforcement, and heightened demands for support from a limited number of other persons. Falkner and Light (1986) discussed the causal mechanisms that may account for the development of hypertension under conditions of prolonged environ-

Figure 5.2 Multiple causal paths between two variables; one path is shared.

104

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

mental stress. They noted that exposure to severe and/or prolonged environmental stressors can affect baroreceptor sensitivity (an internal neuronal regulatory system for blood pressure control), sodium and water retention, circulating catecholamines, and autonomic nervous system functioning. The effect of stress on renal functioning seems particularly important. Krantz, Grungerg, & Baum (1985) noted several paths through which negative life stressors could affect health. These authors suggested that severe stressors could affect physiological variables such as renal hormones, cortisol, lymphocyte levels, and the production of interferon, as well as damage immunologically related tissue. Additionally, life stressors could also trigger important behavioral and social and interpersonal changes conducive to the development of health problems. The multiple paths hypothesized to account for the adverse interpersonal effects of child abuse were noted in previous chapters (Brunk et al., 1987; George & Main, 1979; Harter et al., 1988). Sexually or physically abusive experiences may affect later social relationships through the mechanisms of expectancies, conditioned

fear responses,

the acquisition of social-avoidant behaviors,

self-

labels, and nonresponsiveness to the positive interpersonal behaviors of others.

Implications

Clinical Considerations The idea that a causal variable can affect a behavior disorder through multiple paths has important clinical implications. First, because many interventions operate through the modification of causal mechanisms, the identification of multiple causal mechanisms is often an essential component of preintervention assessment. The question frequently addressed in these assessments ‘‘How does variable A affect the behavior disorder?’’ is expanded to ‘‘In what additional ways does variable A affect the behavior disorder?’’ Second, the behavioral scientist-practitioner should avoid adopting a narrow treatment focus with a client, even if one causal variable seems prominent. That single causal variable is likely to operate through several causal mechanisms and to require broadly focused assessment and intervention strategies. Third, if multiple causal paths are operational for a client, interventions focused on a limited number of those paths may have limited effectiveness. Successful mitigation of causal influence between two variables through one path may not mitigate the causal influence through alternative paths. It may be possible, for example,

to successfully decrease

a client’s self-blame

associated

with sexual abuse experiences without a concomitant reduction in socialavoidant behaviors. When a concurrent focus on numerous causal paths is unfeasible in clinical intervention efforts (e.g., when too many causal paths are operating), a sequential focus or a targeting of those paths with the strongest weights may be necessary.

MULTIPLE AND IDIOSYNCRATIC CAUSAL VARIABLES

105

Fourth, the identification of causal paths is particularly important when the causal variable is resistant to modification.

For example, the genetic determi-

nants of disorders such as diabetes, hypertension, bipolar affective disorders, or some developmental disabilities are not currently susceptible to modification. However,

by identifying

the multiple causal

neurotransmitter,

structural,

or

hormonal paths through which these genetic factors produce their effects, it may be possible to moderate their impact. The identification of causal paths is critical for moderating the effects of other causal variables such as crime victimization, stressful rejection, physical injury, aging, poverty, neurological impairment, intrauterine trauma, a delinquent social environment, divorce, and academic and occupational failure. In each case, the causal variable is not amenable to modification, but the effects

of the causal variable may be attenuated through the identification and modification of their mechanisms of action. Fifth, the identification of causal mechanisms can facilitate the prevention of behavior disorders. If we can also understand how, for example, relapse by a treated alcoholic occurs when he or she is exposed to a social situation where others are drinking, we can focus our prevention efforts on that causal mechanism. The identification of causal mechanisms is particularly important when the occurrence of causal variables cannot easily be controlled. It is difficult to prevent the occurrence of failure and rejection experiences, physical injury, sickness, age-related behavioral and cognitive limitations, loss of friends, criticism,

ambiguous social situations, bad teachers, or interpersonal altercations. Therefore, it is important for the behavioral scientist-practitioner to identify the mechanisms through which these causal variables operate so that their impacts can be mediated. Finally, causal mechanisms should also be carefully examined in social planning (Asher, 1976). For example, it is important to inquire how higher teacher salaries would facilitate student academic achievement, how telephone ‘‘hot lines’’ would decrease suicide rates, and how drug education in grade schools would decrease drug experimentation. Many social interventions are proposed without a scholarly examination of either the causal mechanisms involved in the problems they are intended to address or the mechanisms through which the interventions operate. Maybe “‘insufficient knowledge about drug effects”’ is not responsible for drug experimentation by school children.

Additional Considerations The number of paths in a causal relationship is partially determined by the level of analysis. Higher-level causal variables often include a causal path composed of multiple lower-level causal paths. As noted previously ‘‘immune system dysfunctions’’ may be proposed as one causal path accounting for the relationship between prolonged life stressors and disease. However, this is a

106

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

relatively higher-level causal path in that it can be broken down into several lower-level causal paths such as the release of corticosteroids, continuous activation of the sympathetic division of the autonomic nervous system, suppression of gamma globulin formation, a reduction of eosinophil count, T-cell and killer cell formation, and other deficiencies in white blood cell actions (Asterita, 1985; Miller, 1983; Riley, 1981). Identification of lower-level causal mech-

anisms is important because it can accentuate an array of possible intervention strategies. As noted also in previous chapters, the optimal level of paths in causal models can vary and a causal path should be expressed at a level that is most useful for its intended purpose. Hypothesized causal paths should promote valid measurement, clinical decision making, and precise communication

among behav-

ioral scientist-practitioners about the causes and treatment of behavior disorders. In most cases,

lower-level

causal paths are more

congruent with these

goals than are higher-level causal paths. However, care must be exercised to avoid debilitatingly reductionistic models. Causal paths can be, but are not necessarily, independent (Davis, 1985). Two paths may be correlated because they are both a function of a lower-level causal variable or because they are different expressions of the same causal mechanisms. Let’s take the multivariate effects of a debilitating physical injury as an example. Physical injury may trigger biochemical, subjective, behavioral, and cognitive mechanisms leading to a variety of behavior problems. These mechanisms of action are correlated to the degree that they are triggered by the same causal variables. However, each is also affected by separate variables (e.g., the degree of physical restriction in a burn case may also be affected by an exercise regimen) and, therefore, operate somewhat independently from the others. The main point is that independent causal mechanisms require independent intervention efforts. Causal paths may also be interdependent when one functions as a partial determinant of the other. For example, both negative social outcome expectancies and conditioned social-avoidance behaviors may function as causal mechanisms for a person’s social isolation. Additionally, the probability of social-avoidance behaviors occurring in a particular situation may be partially influenced by outcome expectancies. As pointed out by Davis (1985), causal mechanisms can also vary in the direction of their effects. This is illustrated by physiological responses to physical and psychosocial stressors (Asterita, 1985). For example, laboratory stressors (such as mental arithmetic) may trigger both sympathetic and parasympathetic activation. Sympathetic activation is associated with increased heart rate and blood pressure (along with many other responses) while parasympathetic activation is associated with decreases in those responses. Although stressors tend to result in a greater increase in sympathetic than in parasympathetic activation, the final effect is an additive function of the two effects. Similarly,

MULTIPLE AND IDIOSYNCRATIC CAUSAL VARIABLES

107

the death of a family member may trigger functional self-statements about one’s coping abilities as well as dysfunctional causal attributions about the death. The probability or duration of depression is likely to be an additive function of these two causal mechanisms. These examples demonstrate an important principle—the strength of a causal relationship between two variables is a partial function of the sum of the strengths of their causal paths. The dynamic nature of causal models suggests that the importance of individual causal paths is likely to change over time. For example, the relative importance of specific causal mechanisms would be expected to change over the course of clinical intervention. The adverse impact of a parent’s life stressors on his or her parenting skills may be decreased (i.e., he or she is less likely to be angry or neglectful of his or her child following a stressful day at work; Wahler & Dumas, 1989) by teaching him or her how to better track the child’s behavior, even when the parent is under stress. As a result of this successful intervention, the relative strength of other causal mechanisms for the causal relationship between extraneous stress and child behavior problems (e.g., a higher probability of noncontingent punishment) increases. Furthermore, the importance of individual causal variables is likely to change over time. With a reduction in the magnitude of life stressors the parents’ behavior may become more strongly influenced by the behavior of the child (a change that would be viewed as positive by most systems and interbehavioral theorists, Kantor,

1959: Wahler & Dumas,

1989). Thus, natural fluctuations in

life events, chance occurrences of important causal variables, variability in the operation of mediating variables, and clinical intervention can result in a reduced importance of some causal variables and mechanisms but an enhanced importance of others. The dynamic qualities of causal models are also accentuated with the inclusion of multiple variables (Levins, 1974; May, 1971). Multivariate models are more unstable than univariate models because there is increased chance that some variable in the model will change.

Summary A causal relationship may operate through multiple causal mechanisms of varying strengths. This has important clinical implications because many interventions operate by directly or indirectly modifying causal mechanisms. It also accents the importance of multivariate assessment and broad-spectrum intervention strategies. Attention to causal paths is particularly important when triggering variables are difficult to modify and when prevention and social policy efforts are being instituted. The number of causal paths is related to the level of a causal model. In most cases, lower-level causal paths will be more clinically useful than will higher-

108

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

level causal paths. Also, some causal mechanisms may function either independently or interdependently and can vary in their direction of effect. The strength of causal variables and mechanisms can change over time.

INDIVIDUAL DIFFERENCES IN THE STRENGTH OF CAUSAL RELATIONSHIPS A corollary of the concepts of multiple and differentially weighted causal variables and paths is the idea that there are important individual differences in causal relationships: Individuals exhibiting the same behavior problem may differ in the causal variables affecting that behavior problem, the mechanisms through which the causal relationships operate, and the importance of various causal variables and paths. The idea that causal relationships are idiomorphic across individuals with the same disorder is frequently, but not universally, recognized. Many psychopathology theorists and researchers (Bem & Funder, 1978; Hawkins, 1986; Haynes, 1986a; Kantor, Striegel-Moore,

1959; Khouri & Akiskal, 1986; Marlatt, 1985; Nezu, 1987; Silberstein, & Rodun, 1986; Zucker & Gomberg, 1986) have

stressed the idiosyncratic nature of causal relationships for different individuals with the same

behavior disorder.

However,

many

causal models

of behavior

disorders do not recognize or emphasize individual differences in causal variables and mechanisms. In the latter case, causal models are presumed to be generalizable across individuals. They are, by definition, nomothetic causal models. In some cases the same variables are even presumed to be operational across behavior disorders, as well as across persons with the same disorder (see chapter 3). The idea that causal variables can differ in strength across individuals with the same behavior disorder has been supported in thousands of studies and acknowledged in hundreds of review articles and chapters: Individuals with the same behavior disorder can differ on the number of causal variables of which their disorders are a function; on which variables affect the onset, magnitude, and duration of their disorder; on the relative strength of individual causal variables; in the operation of mediating variables; in predisposition and vulnerability to particular events (see chapter 6); in the setting generality of causal relationships; and in the paths through which causal effects occur. Childhood conduct problems again provide an example. For some parents significant stressors outside of the family may impede their attention and ‘‘surveillance’’ of their child and consequently impede their parenting skills (Wahier & Dumas, 1989). Other parents may lack basic knowledge of child-rearing skills (e.g., emit a low rate of contingent praise to their child). In still other

cases, the conduct problems may be influenced by parental drug abuse, neurological impairment of the child, parental divorce and separation, and peer group interactions. The result of all permutations of these causal variables may be a child who emits similar antisocial, noncompliant, and aggressive behaviors.

MULTIPLE AND IDIOSYNCRATIC CAUSAL VARIABLES

109

Implications

Assessment and Treatment The idea that multiple causal factors and causal paths can be differentially weighted and that there are individual differences in these weights further emphasizes the importance of multifocused and multimodal preintervention assessment to identify the most important causal variables and causal paths operating for a client. Because intervention efforts are often focused on the causal variables and paths that are most strongly weighted, the task of the behavioral scientist-practitioner is to estimate the individual weights of multiple variables and paths prior to intervention for each client. The assumptions of multiple and idiosyncratic causality also diminishes the clinical utility of diagnosis. Intervention programs cannot be based solely on a diagnostic or classification category such as “‘depression’’ or ‘‘attention-deficit disorder’’ because such topographically based diagnoses do not identify which of many possible determinants are operational for a particular client. Diagnoses typically provide oniy an array of possible causal factors. The generalizability of the suggested variables and weights to a particular client cannot be presumed. Thus, nomothetic causal models provide a guide for clinical assessment efforts, but one that should conservatively be applied. As noted in Haynes (1986a), diagnosis can facilitate the design of interven-

tion programs only if any of three conditions are met: (1) specific causal paths are invariably associated with specific diagnostic categories, (2) a hierarchy of the most probable causal paths or their weights is associated with specific diagnostic categories, and/or (3) effective interventions are available for specific diagnostic categories regardless of within-category variance in causality. These conditions are seldom met. Because of the lack of treatment utility for descriptively based diagnostic systems, some investigators have proposed diagnostic systems based on etiology rather than topography (Adams & Haber, 1984; Zucker, 1987). However, these systems have not been validated or refined. Idiosyncratic causality of behavior disorders cautions against, but does not preclude the development of, standardized treatment protocols for individuals manifesting the same disorder. Examples of successful application of standardized treatment protocols are widespread. It does imply, however, that the probability and degree of treatment effectiveness can be significantly enhanced if treatments are tailored to fit the idiosyncratic determinants identified in pretreatment behavioral assessment.

Developing Causal Models There are several methods for estimating the relative strength of causal relationships for a behavior disorder: (1) For nomothetic purposes, persons with and without a particular disorder (or persons who vary on a parameter of a particular disorder) can be contrasted on a number of possible causal variables.

(2) Path analysis and other regression techniques can be used to evaluate the

110

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

“fit’’ of hypothesized and obtained multivariate causal models. (3) Several causal variables can be independently manipulated, in sequence, within a carefully controlled single-subject design. (4) Several causal relationships for one person can be examined across time using time-series designs. In clinical settings with a client, the behavioral scientist-practitioner can collect self-reported or self-monitored data on several possible causal variables (Ciminero, Calhoun, & Adams,

1986; Haynes,

1990). Alternatively, more costly

observational, manipulation, or time-series designs can be used to estimate the strength of causal relationships. As noted previously these methods vary in their power, applicability in clinical situations, and ability to rule out alternative explanations for the observed associations. Individual differences in causal relationships do not preclude hierarchies of probable causal variables for each behavior disorder. For example, theorists from many construct systems would agree that depression often results from personal loss through rejection, death, or separation. These causal models of depression do not guarantee that ‘‘personal loss’’ will be an important causal variable for every case of depression. They do suggest that assessing the role of this variable will often be clinically useful. Thus, hierarchies of causal vari-

ables, inherent in nomothetic causal models, are useful in that they provide cues to the behavioral scientist-practitioner about the probability that particular variables may have important causal functions. However, their rigid application will result in the omission of important causal variables and relationships and limited intervention effectiveness for some clients.

The Determinants of Individual Differences in

Causal Relationships Individual differences in causal relationships may be a result of several factors. First, the relative strength of causal variables may be affected by psychosocial learning experiences. Experience with humiliating social rejection may affect the impact of later rejection experiences. An early history of diets high in simple carbohydrates may heighten affinity for these foods. Growing up with suspicious parents may affect a person’s attributions about the behavior of others or the impact of ambiguous social situations. As discussed in chapter 9, experiences

during critical or sensitive periods (Bornstein,

1989; Colombo,

1982; Scott, 1978) may have particularly powerful effects on the strength of causal relationships. Second, the relative strength of causal variables and paths may be affected by the differential operation of mediating variables across persons, as discussed in chapter 6. Mediating variables can include genetic factors, cognitive variables, social and interpersonal resources, physiological states, and coping skills. Third, the temporal characteristics of causal relationships may affect their strength. For example, for some persons, the immediate mood-enhancing effects of ethyl alcohol may be a far stronger determinant of use than its aversive

MULTIPLE AND IDIOSYNCRATIC CAUSAL VARIABLES

111

but delayed effects. Similarly, immediate outcome often appears to more strongly influence impulsive behavior disorders than does delayed outcome. Fourth, individual differences in the strength of a causal relationship may be a function of differences in the parameters of causal variables. The intensity, frequency, variability, and duration of a causal variation may differ across persons and influence the relative impact of the variable. Thus, some

individual

differences are attributable to the characteristics of the causal variable rather than to differences in responses to that variable. Fifth, persons may differ in physiological vulnerabilities. Differences between persons in the rate at which they metabolize ethyl alcohol, generate neurotransmitter precursors, or exhibit cardiovascular stress responses may account for significant differences in response to an environmental stressor. Finally, some potentially powerful causal variables may not be extant for an individual. Although personal loss may be a potential trigger of depression for a client, a depressive episode is not necessarily a function of that trigger.

CONCLUSION Synthetic causal models of behavior disorders increasingly include multiple causal variables and multiple mechanisms for causal relationships. The degree to which a behavior disorder is considered to be a function of multiple, rather than single determinants, and the number of causal paths operating is partially influenced by the level of the causal variables invoked in the causal model. Generally, lower-level variables and paths are more clinically useful but introduce greater complexity into the model. Additionally, some disorders for some persons result from one or a few causal variables. Multiple causal factors often operate through common pathways and can interact in complex ways, and the operation of one causal variable can mask the operation of others. The operation of multiple causal variables emphasizes the probabilistic nature of causal relationships and the importance of broadly focused assessment and intervention. Causal variables also vary in their strength or importance across disorders and across persons with the same disorder. However, identifying the relative importance among multiple causal factors can be cumbersome, particular in clinical situations.

Chapter 6

INTERACTIVE CAUSAL RELATIONSHIPS

The previous chapter stressed the importance of multiple causal variables for behavior disorders. This chapter will examine some of the ways in which these causal variables interact. The first section examines interactive causal relationships. Subsequent sections examine the related concepts of mediating causal variables and vulnerability to behavior disorders.

INTERACTIVE CAUSALITY When a behavior disorder is a function of multiple causal variables, the causal effects of each variable are often affected by other causal variables. Furthermore, the effects of the variables in combination often cannot be predicted by simply summing their independent effects. For example, Schlundt, Johnson, and Jarrell (1986), in a longitudinal study of bulimic clients, found that the probability of purging following a meal was significantly related to a history of recent purges (i.e., purging tended to occur in cycles). However, the strength of the relationship between those two variables (i.e., the probability that a purge would be followed by another purge after the subsequent meal) was significantly affected by the social context within which eating occurred. The chance of purging was higher when the person had recently purged, but especially so if the person ate alone. In this example, the impact of each causal variable (purging history, social context) on purging was significantly influenced by the other—the two variables interacted to affect the likelihood of purging. Furthermore, without considering recent purges, knowing whether someone ate alone or with others would not be sufficient to predict purging episodes accurately. One could not simply iP

INTERACTIVE CAUSAL RELATIONSHIPS

113

“add up”’ the conditional probabilities of purging associated with the two variables and accurately predict the chance that a client would purge. As illustrated in the foregoing example and in Figures 1.1 (top panel), 2.5 (bottom panel), and 5.2, interactive effects can be inferred when the causal effects of one variable are significantly influenced by the values of another variable

(called the interactive

variable).

In other words,

interactive effects

occur when the conditional probability of a behavior disorder, as a function of one causal variable, is significantly affected by the values of another causal variable, such that Fk

(6-1)

That is, interactive causal relationships have a multiplicative rather than an additive form (Bunge,

1963).

Figure 6.3 also illustrates interactive causal relationships—a diathesis-stress model of psychopathology. This model hypothesizes that environmental stressors and physiological vulnerability interact to affect the parameters of a behavior disorder. The diathesis-stress model is interactive because the relationship between a behavior disorder (Y) and an environmental stressor (X,) is affected

by genetic and physiological vulnerabilities (X>). These examples also illustrate a point made several times in previous chapters—that the effects of a causal variable in an interactive relationship can be conditional.

In reference to equation 6—1, if the value of X> is 0, X; has no

impact on Y;. Thus, interactive causal relationships can involve a necessary causal variable. The strength of two interactive variables may also differ, as indicated by the subscripts a and b in equation 6-1.

Additional Examples of Interactive Causal Models Interactive causal models, referred to as ‘‘multiplicative’’ models by Blalock (1964), “‘interdependent’’ models by Bunge (1959), and ‘‘synergistic effects’’ by others (Krantz, Grunberg, & Baum, 1985; Myrtek & Spital, 1986; Perkins, 1985; Russo & Budd, 1986) are common in the behavioral and social sciences. They have been invoked in models of schizophrenia, cardiovascular disease, substance abuse, feeding disorders of childhood, posttraumatic stress disorder (PTSD), child abuse, childhood depression, paranoia, and depression (Adams & Sutker, 1984; Davison & Neale, 1990; Haynes & Gannon, 1981; Millon & Klerman, 1986; Rosenhan & Seligman, 1989). Perhaps the most common interactive causal models in the behavioral sciences are those that invoke an organism-environment interaction to account for the development of behavior disorders (Bandura, 1981; Bowers, 1973; Bronfenbrenner,

1979; Campbell,

1984; Cohen & Edwards,

1986; Ehlers, Frank, &

Kupfer, 1988; Ekehammer, 1974; Elliott, Trief, & Stein, 1985; Endler & Magnusson, 1976; Mischell, 1977; Nezu, 1987; Pogue-Geile & Harrow, 1984).

114

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

These models can be complex and differ significantly in the classes of variables they emphasize. However, all include the idea that there are individual differences in personality, behavior patterns, cognitive schema, social-environmental

context, and/or genetic-physiological states that affect how an individual responds to an environmental event. These models are often invoked to account for individual differences in response to life stressors such as physical trauma, illness, or death of a family member. For example, depression has frequently been conceptualized within interactive causal models (Akiskal et al., 1980; Barnett & Gotlib, 1988; Beck, 1972; Ehlers, Frank, & Kupfer, 1988; Hyland, 1987; Khouri & Akiskal, 1986; Klerman & Weissman, 1986; Nezu, 1987; Persons & Rao, 1985; Rosenthal & Ro-

senthal, 1985). These models propose that the probability (or intensity or duration) of depression in response to significant life losses or other events is strongly influenced by an individual’s cognitive schemata, degree of interpersonal dependency, level of coping skills, problem-solving abilities, self-efficacy and outcome beliefs, causal attributions, level of perceived social support, genetic predisposition, self-instructions, previous experiences with loss, and/or social skills. There are other examples of interactive causal models of behavior disorders. Brownell and colleagues (1986) examined the determinants of relapse in addictive behaviors following treatment. They noted that multiple causal factors were involved and that although environmental and social factors provided the ‘‘setting’’ for relapse, the level of the client’s coping skills strongly determined whether a small ‘‘lapse’’ turned into a major relapse. Similarly, Bushbaum (1983), in discussing biological bases of psychopathology, proposed that the probability of psychopathological symptoms appearing during periods of chronic life stress was influenced by biological factors, such as the person’s level of monoamine oxidase. Dahl, Heinek, and Tassinari (1962) hypothesized that salt ingestion would affect blood pressure levels but only for genetically predisposed individuals. Similar interactive causal models of behavior disorders have been proposed for schizophrenia

(Goldstein,

1988;

Mirsky

&

Duncan,

1986),

alcoholism

(Goodwin et al., 1977a, 1977b), posttreatment relapse (Marlatt, 1985), the behavioral effects of drugs (Donovan & Chaney, 1985), conditioned nausea responses to chemotherapy (van Komen & Redd, 1985), overeating (Frost, Golkasian, Ely, & Blanchard, 1982), childhood psychopathology (Campbell, 1984), cardiovascular reactivity (Falkner & Light, 1986), psychophysiologic disorders (Haynes & Gannon,

1981), sexual dysfunctions (Barlow,

1986), ir-

ritable bowel syndrome (Sammons & Karoly, 1987), and a variety of healthrelated problems (Krantz et al., 1985).

All these examples illustrate the basic tenet of interactive causal relationships: The causal effects of one variable on a behavior disorder are significantly influenced by the values of another variable.

INTERACTIVE CAUSAL RELATIONSHIPS

115

Complex Forms of Interactive Causal Relationships As suggested earlier in this chapter, interactive causal relationships may also assume complex forms. For example, Miller (1983) hypothesized a three-way interactive causal relationship for behavior disorders: Psychological stresses (X) interact with physiological stresses (X>) in persons with genetic susceptibility (X3) to produce medical and psychological dysfunctions (Y = X; X Xz X X3). Rosenthal and Rosenthal (1985) hypothesized a three-way interaction involving

environmental stressors, cognitive factors, and genetic predisposition to account for various disorders such as affective dysfunctions or immune imbalances. Brunk, Henggeler, and Whelan (1987) proposed a multivariate interactive causal model of child abuse involving the background of the parent, family relations, family transactions, extrafamilial relations, and cultural values. It is reasonable to suppose that many behavior disorders are a function of interactions between more than two causal variables. Although these more complex interactions can present conceptual difficulties for the behavioral scientist-practitioner, their principles of operation remain the same as those for two variable interactions: The strength of effect of a causal variable (or the form of the causal relationship) is affected by the values of other variables. Interactive causal relationships need not be linear in form. For example, the

relationship between Y and X, may be affected by a third variable, but in a quadratic fashion, such as

Y = aX, X bX}

(6-2)

This functional equation implies that the impact of X> on the relationship between Y and X, varies in a nonlinear, quadratic fashion. Figure 6.1 illustrates one possibie form of equation 6—2: The degree of impact of X2 on the relationship between X, increases as the values of X> increase. Interactive causal relationships may also assume parabolic and other nonlinear forms. In a parabolic relationship, there is a negatively accelerating effect of a third causal variable on the relationship between two other variables (see Figure 6.3 on the diathesis-stress relationship). This suggests that the degree of impact of the third variable is least at lower levels, increases at a decreasing rate as its values increase, and reaches a point where increasing values of the variable have minimal additional effects on the relationship between the other two variables. For example, running or other aerobic exercise may moderate the eftect of life stressors on hypertension (Danforth et al., 1990; Doyne, Chambless, & Beutler, 1983; Hagberg & Seals, 1986). But, as the amount of aerobic exercise

increases, the moderating impact on responses to stress accelerates more slowly. There is probably a point where additional exercise is associated with very little additional benefit. Chapter 9 addresses nonlinear causal relationships in more detail.

116

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

1 Figure 6.1 Two-way quadratic interaction. The relationship between X, and Y is a quadratic function of the values of Xp.

As suggested in chapter 2, interactive causal relationships are further complicated by the bidirectional or reciprocal form they sometimes assume. For a bidirectional causal relationship between X, and Y, the interactive effects of X>

may differ for the two directions of causal influence. For example, the impact of a client’s aversive marital interactions on his or her magnitude of depression may be strongly affected by the degree of social support received outside of the marriage (e.g., marital distress would lead to depression only under conditions of low social support). However, the impact of the client’s depression on marital interactions may be only weakly affected by his or her degree of social support (see discussions of the relationship between marital distress and depression by Beach & O’Leary, 1986, and O’Leary & Beach, 1990).

Mechanisms of Interactive Causality What accounts for interactive causal effects? How can one variable affect the causal relationship between two others? As suggested by Falkner and Light (1986) and McKinney

(1988), interactive causal effects can occur when two

causal variables operate through the same causal mechanism. Although the operation of two variables through a common causal mechanism may not be a necessary condition for interactive effects, it probably facilitates such effects. For example, the common causal pathway for the interactive effects of marital distress and social support on depression may be the ‘‘rate of positive interpersonal exchanges.’’ Marital distress and loss of social support are usually

INTERACTIVE CAUSAL RELATIONSHIPS

(V4

accompanied by a reduced rate of positive interpersonal exchanges, which, in turn, has been associated with depressed effect. Consequently, the impact of marital distress on a client (e.g., the probability the client will become depressed) will be affected by the amount of support received from the client’s family members and friends. The relationship between social support and marital distress would be ‘‘interactive’’ rather than ‘‘additive’’ if both affected the same causal mechanism. Variables operating through different causal mechanisms would more likely have additive effects because the operation of one would not necessarily mediate the causal effects of the other. In interactive causality, one or more variables can also function as a catalytic variable to enable the effects of the other. With catalytic causal relationships, neither variable alone is sufficient to cause a behavior disorder. The presence of one variable (or a particular value of the variable) enables a causal relation-

ship between the other two. For example, in the diathesis-stress model of psychopathology (Hollandsworth, 1990), a genetic predisposition ‘‘enables’’ a causal relationship between environmental stressors and schizophrenic behaviors). The identification of interactive causal effects can also aid in the identification of causal mechanisms. If two causal variables interact to affect a behavior disorder, it is useful to search for a common causal pathway. What common mechanism underlies the interactive effects of rapid eye movement (REM) sleep and life stressors on migraine headache (Sexton-Radek, 1989)? As these examples illustrate, the identification of common pathways underlying interactive causal relationships sometimes requires that causal variables be reduced to a common class and level. For example, the common mechanism of action for the interactive variables of sodium intake and chronic life stress (on responses to transient stressors) may be most easily understood if the actions of both variables are examined at a cellular level. Without examining causal relationships at a common level, it is particularly difficult to explain interactions between different classes (e.g., cognitive, social-environmental) of causal variables. Analogously, in trying to understand the mechanisms underlying a causal relationship, it will often pay off to examine the operation of other causal variables that interact with the variable of interest. For example, to understand the mechanisms through which exercise mediates the effects of psychological stressors on mood, it might be helpful to examine the mechanisms through which stressors (an interactive variable) affect mood

(the mechanisms

might involve

norepinephrine, corticosteroids, endorphins, ANS activity, attentional factors). If the two variables act through a common pathway, knowledge of one causal mechanism may aid in understanding the other. Similarly, we may suspect common mechanisms of action for different classes of causal variables that interact in their effects on a behavior disorder. For example,

similar mechanisms

of action may underlie the interactive effects of

118

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

both attentional focusing and performance anxiety in sexual dysfunctions (Barlow, 1986), genetic and psychosocial stressor variables in bipolar affective disorders (DePue et al., 1981), dieting cycles, metabolic factors and psychosocial stressors in overeating (Brownell & Foreyt, 1985), social support and mood and physiological

dependence

in substance

abuse

(Marlatt,

1985; McCrady,

1985), early separation and later personal losses in depression (McKinney, 1988), attributional style and negative life events in depression (Persons & Rao, 1985), parental discipline and peer rejection in antisocial behavior (Patterson, 1986), hormonal and cognitive factors in diet-binge cycles (Polivy & Herman, 1985), and environmental strains and genetically determined vulnerability in a variety of behavior disorders (Zubin, 1986).

Bidirectional causal relationships can, but need not, involve common causal mechanisms. For example, response contingencies may be the mechanism of action through which a parent’s disciplinary behavior reduces a child’s aggressive behavior. But reduced aggressive behavior by the child may affect the parent by reducing the rate of noncontingent aversive stimuli in the parent’s environment. In this case a bidirectional causal relationship between the parent’s and child’s behavior occurs through different paths.

The Strength of Interactive Causal Relationships Although causal relationships may be discontinuous and nonlinear (see chapters 7 through 9), the strength of interactive effects depends mostly on their parameter values. For example, the degree to which self-statements can moderate the effect of pain on recreational and social behaviors

(Martelli, Auer-

bach, Alexander, & Mercuri, 1987) would be expected to vary with the frequency with which a person invokes the statements, the number of settings in which they are employed, and their intensity and diversity. Furthermore,

if interactive

effects

occur

through

common

causal

mecha-

nisms, the relative impact of each causal variable depends upon the proportion of variance in the causal mechanism that it controls. In the example provided earlier, the relative impact of marital distress and loss of social support on depression (the degree to which each causal variable can mediate the causal effects of the other), would be expected to vary with the degree to which positive interpersonal exchanges (the presumed causal mechanism) is controlled by each. If a major proportion of a person’s positive interpersonal exchanges comes from friends and family outside of the marriage, the independent and interactive effects of a distressed marital relationship will be significantly less than if positive interpersonal exchanges come primarily from the marital relationship. Because a causal relationship can involve multiple causal paths, the strength of the interactive effects is also a function of (1) the degree to which their mechanisms of action overlap and (2) the relative strength of the common and

INTERACTIVE CAUSAL

RELATIONSHIPS

119

independent causal paths. First, as illustrated in Figure 6.2, two causal variables may share some, but not all, causal paths. Furthermore, with other conditions being equal, the interactive effects of X2 are probably greater in A than in B because X> influences the relationship between X, and Y through more causal paths in A. Additionally, the interactive effects of X. would be expected to be greater if causal path c is more important than causal path a. The strength of interactive causal relationships is dynamic, unstable, and conditional. Interactive effects of a causal variable can vary across behavior disorders, persons, time, or settings. For example, self-delivered relaxation instructions may more effectively moderate the arousing effects of negative rumination during classroom test situations than at night while trying to go to sleep. Example

Example

A

B

Figure 6.2 If the strength of causal paths are equal for A and B, the interactive causal relationship is stronger in A than in B, because the causal variables in A share more common causal paths.

120

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

Interactive and Additive Causal Relationships Multiple causal variables can affect a behavior disorder in an additive and/or interactive manner. Unlike interactive causal relationships, additive causal relationships involve causal variables whose independent effects are not modified when they occur concurrently: An additive causal variable does not change the strength or the form of other causal relationships; it simply introduces an additive component to the causal model. Consequently, the effects of additive causal variables can often be predicted by summing their independent effects.! Often, causal variables that are considered to have additive effects are actually elements of a class of causal variables. For example, a series of different daily aversive events (e.g., fights with a sibling at home, a traffic accident)

may summate to affect the positive and negative symptoms of a schizophrenic patient living at home, the likelihood of smoking, or the development of psychophysiological disorders (Haynes & Gannon,

1981; Mirsky & Duncan,

1986;

Thoits, 1983). While each event may be considered as a separate causal variable, they may also be considered to be different elements of the same class of the causal variable ‘‘life stressors’’ and, therefore, summate in their effects. There are numerous examples of causal models of behavior disorders that include additive elements of a class of causal variables. For some persons the probability of acquiring a migraine headache may be an additive function of the number of vasoactive foods consumed (Blau & Diamond,

1985; Hanington,

1980). The probability of sleep-onset difficulties may sometimes be an additive function of the amount of stimulant-containing foods and beverages ingested prior to bedtime (Youkilis & Bootzin, 1981). Miller (1983) suggested that for some persons, the probability of acquiring a conditioned fear response may be an additive function of the frequency of conditioning. The effect of life stressors On immune system functioning and neurotransmitter deficiencies may be an additive function of their accumulated

duration (Miller,

1983). Kirsch (1985)

hypothesized a general additive function when he suggested that the probability of any particular volitional behavior is the sum total of the expected reinforcements for that response. Also, the probability or ‘“‘depth’’ of depression may be an additive function of the number of irrational beliefs endorsed (Beck, 1972). Finally, domestic violence may, in some families, be an additive func-

tion of a series of escalating angry verbal exchanges between spouses (Maiuro et al., 1988). Additive relationships between different causal variables can also occur when they operate through independent causal mechanisms. For example, Goldstein (1988) suggested that medication compliance and family expressed emotion (i.e., degree of support, overinvolvement, coercive interactions) summate in their effects on treatment maintenance for schizophrenic patients. Mirsky and Duncan (1986) suggested that numerous risk factors for schizophrenia may summate to affect the chance of symptom display or the intensity of symptoms.

INTERACTIVE CAUSAL

RELATIONSHIPS

121

They suggested that more severe stressors were associated with more severe disorders. Striegel-Moore, Silberstein, and Rodin (1986), in their causal analysis of bulimia, invoked an additive model for the conjoint effects of dieting, a family focus on looks, metabolic factors, sex-role patterns, and a media focus on thinness. Because additive causal variables often act through different causal pathways, their effects are often less powerful than are those of interactive causal variables. With additive causal effects one causal variable does not modify the effect of another. Consequently, the final effect reflects the addition or subtraction of the multiple independent effects. Also, additive causal relationships are not necessarily linear. The relative impact of each causal variable will vary with its form. The additive impact of two variables will be nonlinear when one has a nonlinear effect. Furthermore,

as expected in dynamic causal models, the relative impact of additive variables can change over time. Interactive and additive causal relationships are not mutually exclusive. In fact, interactive causal variables often have an additive relationship also. This would be represented by

Y= ak, + PX, + (cx, X .dX>)

(6-4)

interactive relationship with Y. In the example of bulimia presented at the beginning of this chapter, the variables ‘‘the recent history of purging’’ and ‘‘social context’’ independently predict purging, but also interact to affect purging. Both additive and interactive effects would be expected for a variable that operates through multiple causal mechanisms: It will have interactive effects with variables operating through the same path and additive effects with variables operating through different paths. Whether two variables appear to have additive or interactive effects depends on how they are measured. For example, social support may appear to have additive or interactive effects, depending on the construction of the measurement instrument. When structural measures of social support are used (e.g., those that measure frequency of social contacts or the extent of the support network),

simple additive effects will more

likely be noted.

When

functional

measures are used (e.g., those that measure perceived esteem support or information

provision),

interactive

effects

will more

likely be noted.

The general

principle imbedded in this example is very important: The apparent strength of interactive and additive effects of a causal construct depends greatly on how that construct is specified and measured. The parsimony of synthetic causal models with interactive and additive func-

tions should be questioned. Are valid but trivial dimensions introduced into causal models of behavior disorders by differentiating between interactive and additive causal relationships, by suggesting multiple overlapping and nonoverlapping causal mechanisms, and by noting that the form of causal relationships

122

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

can change over time and over the parameters of causal variables? Whether or not these distinctions are warranted depends on their impact on clinical and research decisions. It is the premise of this chapter that such distinctions are warranted, and these issues are addressed more fully in the section “‘Clinical Implications.”’ In summary, additive causal relationships involve causal variables whose effects are independent of the actions of other causal variables. Sometimes, causal variables with additive functions represent different elements of a class of causal variables. Additive relationships can also occur when they operate through nonoverlapping causal mechanisms. Because additive causal variables often act through independent causal pathways, their overall impact on behavior disorders are often less powerful than are those of interactive causal variables. Finally, additive and interactive effects are not mutually exclusive.

Mediating and Moderating Variables The concepts of mediating and moderating variables are closely associated with the concept of interactive causality. A mediating variable is an interactive causal variable: It is one that affects the strength or form of relationship between other variables (Arnold, 1982). A moderating variable (sometimes called

a buffering variable) is also an interactive variable, but with a directional component. A mediating variable can strengthen or weaken the relationship between two other variables. A *‘moderating’’ or ‘‘buffering’’ variable, although inconsistently defined in the psychopathology literature, refers to a variable that weakens another causal relationship. Thus, moderating variables are special cases of mediating (i.e., interactive) variables.

One of the most frequently invoked moderating variables in causal models of psychopathology is social support (see reviews by Cohen & McKay, 1984; Kessler, Price, & Wortman, 1985). Social support (e.g., perceived social support, emotional support, esteem support, social networks, positive social contacts) has been proposed to mediate the effects of life stressors on physical health, substance abuse, depression, child behavior problems, anxiety, psychophysiologic symptoms, mood, posttraumatic stress disorder, anger, and/or pregnancy complications. The term ‘‘mediating variable’’ is sometimes used to refer to an intervening process, often involving a hypothetical construct, as an explanation for a causal relationship (Hyland,

1981; James et al., 1982). In this usage, a con-

struct such as ‘“‘feelings of inadequacy’’ would serve as a mediating hypothetical construct invoked to explain the avoidant behaviors of a shy male in the presence of women. Such a use of the concept is not inconsistent with the definitions proposed here (the variable affects the relationship between ‘‘presence of women’’ and “‘social avoidance’’) but is excessively restrictive be-

INTERACTIVE CAUSAL

RELATIONSHIPS

123

cause it implies a “‘necessary’’ variable or process for a cause-effect relationship to occur. A moderating mechanism was proposed by Pavloski (1989) in his negative feedback control system model. He suggested that intrinsic feedback systems act to moderate the effects of extraneous stimuli to keep behavioral and biological functioning within adaptive ranges. Examples include our biological and behavioral temperature control mechanisms that keep our body temperature within narrow ranges, despite wide fluctuations in environmental temperature. An important component of this negative feedback model, which will be addressed further in the next chapter, is that negative feedback control systems are taxed when extraneous events change too quickly or too much. This suggests that buffering mechanisms have temporal and variable value limits; if events exceed these limits, the buffering effects may be insufficient to prevent biological and behavioral disorders.

Clinical Implications Interactive and additive causal relationships have several important clinical implications. First, the idea that causal variables can interact through common causal paths strongly affects the selection of intervention strategies and targets. An intervention strategy designed to moderate the effects of an original causal variable (e.g., a therapy strategy to moderate the adverse interpersonal effects of a client’s history of sexual abuse) should operate through the same causal mechanism as that variable. If the effects of early childhood sexual abuse involve the mechanism of self-labels or inappropriate social outcome expectancies, interventions that operate on these mechanisms would be expected to be more powerful than interventions operating on other causal mechanisms. Interventions that operate through paths alternative to original or maintaining paths may still be effective. For example, role-playing may be used to enhance social interaction skills of a client for whom self-labels and attributions are the primary causal mechanism for social anxiety. However, interventions that focus on tertiary causal mechanisms would be expected to be less powerful than those that focus on primary causal mechanisms because they leave the primary causal mechanisms unmodified. Similarly, if the causal mechanism for a client experiencing significant difficulty falling asleep because of severe family conflict involves presleep cognitive ruminations about those conflicts, interventions that operate by reducing aversive ruminations about the conflict (in addition to family therapy designed to directly modify the source of the conflict) might be more helpful than interventions that operate through other mechanisms. In this case an example of an intervention that operates through a tertiary causal mechanism would be stimulus control interventions that promote a consistent sleep and bedtime behavior pattern and a consistent association between sleeping and bedroom stimuli

124

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

(Haynes, Adams,

West, Kamens,

& Safranek,

1982). Stimulus-control inter-

ventions would be expected to facilitate improved sleep onset in an additive manner, even when they don’t address the primary causal path of the sleep disorder. In this case, family conflict would still occasion presleep ruminations, even if a more consistent sleep pattern had been adopted. In sum, interventions that function in an interactive manner may be more powerful than those that function only in an additive manner because they moderate the mechanisms underlying the causal relationship. Interventions that target alternative causal mechanisms can also be effective, but their power is diminished because they leave unaffected the causal mechanisms primarily responsible for the behavior disorder. Common causal mechanisms for interacting causal variables can also help to identify a set of potentially effective interventions that operate through the same mechanisms. For example, let’s presume that the effects of an illicit substance is an interactive function of expectancies, social setting, and the physiological state of a person (Baumrind & Moselle, 1985; Huba & Bentler, 1982b; Kirsch, 1985; Lindesmith,

1968; Marlatt et al, 1988). Interactive effects among these

variables suggest that all share at least one causal path (such as ‘‘receptor NH sensitivity,’’ ““‘neurotransmitter output and uptake, attention,’’ or ‘‘labeling’’). If we identify a common path for interactive drug effects, such as ‘‘receptor sensitivity,’ we can also investigate interventions that might effect the same pathway (such as the use of drug agonists). The power of such interventions would be related to the degree they modify the impact of several interacting causal variables through actions on common causal pathways. Second, if we presume that there are multiple causal variables affecting a behavior disorder and that each causal variable may operate through multiple causal paths, the power of an intervention will be related to the number of causal paths through which it operates. Some interventions affect only a few causal paths, while others affect many. Family therapy for anorexia and bulimia (Schlundt et al., 1986; Striegel-Moore et al., 1986), for example,

may

affect multiple causal paths such as the contingencies associated with eating, the client’s body image, the types of foods ingested, the valance of eatingrelated family interactions, and the rate of reinforcing family activities. Assuming that these are important multiple causal paths, family therapy would be expected to be more effective than an intervention that focuses on any one of those paths. Third, the validity of treatment decisions depend on the degree to which the mechanisms of causal influence have been identified. The behavioral scientistpractitioner must continuously ask ‘‘How did loss of a spouse lead to suicide ideation for a client?’’ ‘‘Why does a child’s crying trigger parental violence?’’ These inquiries will enhance the chance that important causal mechanisms will be identified and targeted in intervention. Fourth, the selection of intervention strategies that interact with important

INTERACTIVE CAUSAL

RELATIONSHIPS

125

causal variables is often facilitated by invoking lower-level causal variables. High-level causal constructs such as ‘‘divorce,’’ ‘‘internal attributions,’’ or ‘‘social anxiety’ are useful for the initial identification of an array of possible causal relationships. However, they are usually insufficiently specific to facilitate the development of interactive intervention strategies because specific causal mechanisms operating for a particular person cannot be identified. High-level causal constructs always involve numerous possible causal mechanisms. Without precisely specified causal mechanisms, the selection of an optimally effective interactive intervention strategy is more problematic. Fifth, the operation of multiple causal paths emphasizes the importance of preintervention assessment of multiple variables, using multiple assessment methods. \t also promotes the application of multiple intervention strategies that target these paths (Hawkins, 1986; Haynes, 1978, 1989, 1990; Hersen, 1981; Nelson & Hayes, 1986).

Identifying Interactive Causal Relationships The identification of interactive causal relationships can require complex statistical procedures and experimental designs (Barlow & Hersen, 1984; Cohen & Cohen, 1975; Kazdin, 1982; Kratochwill, 1978; Ostrom, 1978; Pedhazur,

1982) and is beyond the focus of this book. However, several principles of this process should be stressed. First, an interactive effect between two variables refers to the proportion of variance in a functional relationship than can be accounted for by their interaction beyond that accounted for by the independent additive effects. As indicated earlier, variables may have both additive and interactive effects, and we are interested in the degree to which interactive effects between two (or more) variables increase predictive efficacy beyond that associated with the simple additive main effects of the variables? Second, in searching for interactive effects, behavioral scientist-practitioners should attend to the conditional operation of a causal variable. As illustrated in some of the examples provided in this chapter, many causal variables operate only within certain value domains of other variables; this is a strong indication of an interactive relationship between those variables. Unraveling the mechanisms of bidirectional causal relationships and identifying interactive effects in those relationships is particularly difficult. For example, there is evidence for a bidirectional relationship between parental depression and child behavior problems. One mechanism for this relationship may involve changes in disciplinary behaviors by depressed parents: Depressed parents may punish their children less systematically and more harshly. However, the children of depressed parents may also behave in a hostile, noncompliant, socially impaired manner, thereby increasing the sources of parental depression (see review by Downe & Coyne, 1990). To assign causal importance to these two potential causal paths, and to identify other causal variables

126

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

that operate on the same mechanisms, requires carefully designed time-series measurement in which both variables are repeatedly measured across many time points (e.g., Cohn, Campbell, Matias, & Hopkins, 1990; Field, Healy, Goldstein, & Guthertz,

1990). With such a design, it is possible to calculate the

strength of both relationships, ensure that precedence of the causal variable is taken into consideration, and examine the differential effects of mediating variables. Even then, the results are relevant only to understanding the maintenance

of the bidirectional interactions, not their original etiology.

INTERACTIVE CAUSALITY AND VULNERABILITY TO BEHAVIOR DISORDERS Most of us are familiar with friends or family members who are especially prone to withdraw from social contacts when faced with minor professional or personal setbacks. Some are very likely to overeat when bored or anxious or to drink too many

Mai Tais when

faced with a work overload.

In contrast,

other persons exposed to the same life events are much less likely to manifest these behavior problems—this latter group is considered more ‘‘resistant,’’ while the former group is considered more vulnerable to the effects of these events. The concept of vulnerability (sometimes referred to as predisposition) again reflects the conditional nature of behavior disorders. The probability of a behavior problem may be identical for ‘‘resistant’’ and ‘‘vulnerable’’ individuals in the absence of specific triggers or settings. Differential responses occur only when these persons are exposed to specific triggering situations. The concept of vulnerability is also allied to the concepts of ‘‘causal chains,’’ “interactive causal relationships,’’ and ‘‘triggered causal variables.’’ If we reexamine many of the causal models presented in this chapter, we note that the chance of a person becoming violent, hypertensive, or bulimic depends on exposure to an initial causal variable and the operation of mediating variables operating on the causal chain. For example, a patient who is recently released from a psychiatric institution may be particularly vulnerable to relapse when exposed to life stressors (a triggering variable) if he or she lacks a structured and supportive family environment (Goldstein,

1988; Kessler et al., 1985; Mir-

sky & Duncan, 1986). In this case, ‘‘family social support’’ mediates the probability of relapse (i.e., degree of vulnerability) as a function of exposure to life stressors would be considered a vulnerability factor because it affects the probability of relapse. Jones and Barlow (1990) suggested that biological and psychological vulnerabilities are important in the development of anxious apprehension, panic disorder, and posttraumatic stress disorder.

As part of their multivariate, but pri-

marily unidirectional, causal models, these authors suggested that biological vulnerability reflects the likelihood that a person will exhibit elevated and diffuse neurobiological reactions to negative life events. Psychological vulnera-

INTERACTIVE CAUSAL RELATIONSHIPS

WAY

bility reflects the likelihood that a person will view negative life events as unpredictable and/or uncontrollable. Biological vulnerabilities were presumed to possibly result from genetic transmission. Psychological vulnerabilities were presumed to be affected by the degree to which prior experience with negative life events were predictable or controllable. There are other examples of vulnerability in causal models of behavior disorders. The degree to which a person experiencing marital distress or other negative life events is vulnerable to depression may be a partial function of that person’s degree of interpersonal dependency on the spouse, level of perceived social support outside of the marriage, or attributional style (Barnet & Gotlib, 1988; Melatsky, Halberstadt, & Abramson, 1987). A person who is very dependent on his or her spouse for social support may be especially vulnerable to the psychological effects of divorce or separation. Similarly, the probability that exposure to RNA-disrupting viruses will lead to cellular dysfunction may be affected by the state of an organism’s immune system functioning (Riley, 1981). Persons undergoing long-duration stress may be particularly vulnerable to the effects of viral infection because of resulting disruptions in immune system functioning (Asterita, 1985). For some persons relatively trivial difficulties at work or school may lead to major depressive episodes (Rosenthal & Rosenthal, 1985) as a function of other life stressors. Or relatively low-intensity sounds may trigger a startle response in some posttraumatic stress disorder (PTSD) patients (Foa et al., 1989) during elevated states of arousal. Some causal models of eating disorders have proposed that eating restraint may be a vulnerability factor for binge eating under periods of stress (see discussions by Herman & Polivy, 1980, 1984; Hill, Rogers, & Blundell, 1989; Ruderman, 1986). Restrained eaters are persons who are dieting and are preoccupied with weight and body image. These persons may be particularly likely to overeat in response to specific triggering stimuli, such as alcohol ingestion, the intake of high-caloric food, and anxiety. These triggers of overeating episodes are sometimes labeled disinhibitors of dietary restraint.

A Definition of Vulnerability As the foregoing examples suggest, vulnerability can be defined in three ways. First, it can be considered as the probability that a particular causal variable will lead to a particular behavior problem. Second, vulnerability can refer to the magnitude of a causal variable that is necessary to engender a behavior problem. Third, vulnerability can be considered as the unconditional probability that a behavior problem will be manifested. For example, patients

with eating disorders may differ in the likelihood that depressed mood will trigger an eating binge (definition 1), in the magnitude of depressed mood that is necessary to trigger an eating binge (definition 2), or in the likelihood that a binge will occur in any circumstance (definition 3).

128

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

In all three definitions, vulnerability can be considered a mediated function:

The probability of developing a behavior problem in the presence of a triggering causal variable, or unconditionally, is a function of mediating variables. Furthermore,

these mediating variables, or vulnerability factors, can have bi-

polar effects; they can either increase or decrease the impact of other causal variables (Hawkins, 1986). For example, genetic factors may either increase or decrease the probability of developing a conditioned fear response following exposure to a traumatic or noxious event. In most applications, vulnerability factors are presumed to be enduring rather than transient, although not necessarily permanent or unmodifiable. Examples of enduring vulnerability factors include intrauterine and birth traumas, fetal alcohol syndrome, genetically determined metabolic dysfunctions, negative selfconcept, childhood

sexual abuse, childhood obesity, need for perfection, and

early nutritional deficiencies. However, vulnerability may be a transient condition, such as vulnerability to infection during temporary periods of immune suppression or a transient vulnerability to social facilitation of drinking during periods of life stress.

Vulnerability and the Diathesis-Stress Models of Psychopathology Perhaps the most frequently invoked vulnerability models in psychopathology research hypothesize biologically determined vulnerability to the effects of psychosocial stressors—the diathesis-stress model in psychopathology. In these models, genetic and other physiological factors are presumed to affect the chance that a person will manifest schizophrenic symptoms, or other behavior disorders, upon exposure to environmental stressors (Bushbaum, 1983; Depue & Monroe,

1986; Dohrenwend & Shrout,

1985; Gannon,

Kessler et al., 1985; Khouri & Akiskal, Duncan,

1986; Rosenthal & Rosenthal,

1981; Goldstein,

1986; McKinney,

1988;

1988; Mirsky &

1985; Zubin, 1986).

This vulnerability relationship is illustrated in Figure 6.3. This figure illustrates a negative parabolic relationship between the level (e.g., intensity, frequency) of an environmental stressor necessary to engender a behavior disorder and the strength of a genetic-physiological vulnerability factor. Persons scoring on the right side are more vulnerable for the behavior disorder. Therefore, persons scoring lower on the vulnerability factor are less likely to manifest a behavior disorder at a particular magnitude of the triggering stimulus than are persons scoring higher on the vulnerability factor (we are presuming a continuous, rather than dichotomous, vulnerability factor). Conversely, a higher magnitude of stressor is necessary to engender a behavior disorder at a lower than at a higher score on the vulnerability factor. Most diathesis-stress models of behavior disorders suppose that physiologically based vulnerability can be modified through social, behavioral, and cogni-

INTERACTIVE CAUSAL RELATIONSHIPS

129

level stressor oO —_—_——

eS

2:£:£05°5°0:0C

0 1 genetic/ physiological vulnerability factor Figure 6.3 A parabolic relationship between the value (e.g., intensity, frequency) of an environmental stressor necessary to trigger a behavior disorder, as a function of genetic-physiological predisposition. Persons scoring to the right of the curve would be “at risk’’ for the disorder.

tive mechanisms (e.g., Kessler et al., 1985). However, the degree to which vulnerability can be modified may be a function of the strength of the vulnerability factor. For example, Khouri and Akiskal (1986) hypothesized that, for some persons, genetic factors may be sufficiently powerful to preclude significant mediation by nongenetic variables. Thus, it may be difficult to moderate some of the cognitive deficits caused by severe asphyxia during birth (Mirsky & Duncan, 1986). Consequently, children exposed to severe or prolonged oxygen deficits may always be at risk for educational underachievement.

Vulnerability and Latent and Original Causal Variables Vulnerability factors overlap with the concepts of original and latent causal variables, and this overlap helps explain how vulnerability develops. A heightened vulnerability to a disorder is often considered to be a function of early childhood experiences, past traumatic experiences, or stable biological and cognitive dysfunctions. These ‘‘original’’ causal variables, or their effects, are often unobserved (latent) until the person is challenged by a particular environmental stressor. Thus, a person’s negative cognitive schemata, which can prompt depressive episodes, may be inoperative or unmeasurable until triggered by interpersonal stressors (Rush & Beck, 1978). As noted earlier, the clinical assessment of such variables necessitates the application of special assessment conditions—such as role-playing—that evoke the special triggering conditions. Vulnerability to the effects of a particular causal variable may also be af-

130

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

fected by prior exposure to that variable, although the direction, strength, and mediation of the effects of prior exposure are complexly determined. Miller (1983), for example, emphasized the importance of investigating the degree to which prior exposure to a life stressor influences subsequent responses to that stressor. Does experience with prior rejection by a loved one or physical injury increase or decrease one’s responses to the same events in the future? What factors influence the effects of prior exposure to a stressful event? In cases where increased vulnerability to the effects of a triggering causal variable is a function of original causal variables, the heightened vulnerability is most likely a function of intermediate and durable effects of the original causal event. In other words, for a causal event to produce a delayed vulnera-

bility effect, it must produce long-lasting effects that are problematic under specific conditions encountered later in life. Thus, early sexual abuse can result

in a heightened vulnerability for adult relationship dysfunctions only if it has resulted in some enduring cognitive, behavioral, or physiological changes that later interact with relationship variables. In some cases, vulnerability factors are not actually “‘latent’’ but operate on a daily basis. Examples include a person with frequent paranoid interpretation of friends’ behaviors, a tendency to cease active coping responses when confronted with initial failure, a heightened startle response, or transient cardiovascular hyperreactivty. However, despite their frequent occurrence, the effects of these factors may disrupt ongoing social functioning or to lead to behavior disorders only in relatively rare circumstances, such as prolonged physical stress, rejection by a loved one, or extreme difficulty with an antisocial teenaged child. Sometimes there are markers for vulnerability. One frequently invoked marker is a parental history of the targeted disorder. Offspring of schizophrenic parents may be more vulnerable to schizophrenia under conditions of extreme environmental stress (Goldstein, 1988), offspring of hypertensive parents may be more vulnerable to cardiovascular hyperreactivity during transient stressors (Haynes et al., 1991), and offspring of parents with bipolar disorders may be more likely to manifest these disorders under particular circumstances (Beardslee et al., 1983; Rosenthal, Akiskal, Scott-Strauss, Rosenthal, & David, 1981). Additionally, Mirsky and Duncan

(1986) noted that scores on various cognitive

assessment tasks might serve as a marker for cognitive deficits associated with physiological vulnerability to schizophrenia (such as those attributable to intrauterine trauma or asphyxia during birth).

Clinical and Empirical Utility The identification of a vulnerability factor can allow a behavioral scientistpractitioner to predict more accurately the probability that a behavior disorder will occur and the conditions under which it is most likely to occur. For example, we can predict that socially isolated persons have a higher risk for

INTERACTIVE CAUSAL RELATIONSHIPS

131

developing depressive symptoms when they lose a spouse (Cohen, Blake, Cohen, Fromm-Reichmann, & Weigert, 1954) or that cognitively impaired children have a higher chance of manifesting behavior problems in an unstructured learning environment. In these cases, ‘‘social isolation’’ and ‘‘cognitive impairment’’ were considered vulnerability factors because they were associated with the probability of a behavior disorder occurring in a specific context. Most important, from a clinical perspective, vulnerability can sometimes be modified. Vulnerability to a behavior disorder is not necessarily an enduring characteristic of a person, even if the vulnerability factor is enduring. In fact, a reasonable proportion of clinical activities are devoted to reducing the risk of behavior problems occurring in high-risk situations for vulnerable persons (Brown, Lichtenstein, McIntyre, & Harrington-Kostur, 1984; Burish, Carey, Krozely, & Greco,

1987; Donovan & Chaney,

1985; Marlatt, 1985).

Most methods of modifying vulnerabilities involve introducing variables to mediate the effects of the vulnerability factor. The introduction of mediators is particularly important when vulnerability is a function of some difficult-to-modify causal variable, such as early childhood experiences or genetic factors. For example, dietary restrictions on sodium intake can help reduce cardiovascular hyperreactivity to stressors of some persons who are genetically at risk for hypertension. Effective mediation of vulnerability factors are most likely to operate through a common causal mechanism. As explained in previous sections, the task of the behavioral scientist-practitioner is to identify the most likely causal mechanisms of vulnerability factors and introduce variables to mediate the effects of those factors through the same causal mechanism. The identification of vulnerability factors can also promote prevention efforts. Documenting a relationship between alcohol ingestion by pregnant women and later vulnerability of their children to a variety of physiological, cognitive, and behavioral dysfunctions suggests the potential efficacy of efforts designed to reduce alcohol ingestion during pregnancy. An important task of clinical assessment and research is to understand why some persons are particularly vulnerable to the effects of specific causal variables. The identification of the determinants of vulnerability can aid in preventing the occurrence, or modifying other parameters, of the targeted behavior disorders.

CONCLUSION Interactive causal relationships are common in synthetic causal models of behavior disorders and occur when the effects of causal variables in combination cannot be predicted by simply summing their independent effects. More complex forms of interactive causality are possible when more than two vari-

132

MODELS OF CAUSALITY IN PSYCHOPATHOLOGY

ables interact and when the form as well as the strength of causal relationships are influenced. Interactive causal relationships are more likely to occur when multiple causal variables operate through the same causal mechanism. Multiple causal variables that operate through different causal mechanisms are more likely to manifest additive causal relationships. It is logical to presume, therefore, that the strength of interactive causal relationships is a function of the degree to which multiple causal variables overlap in their causal mechanisms. The concept of causal mediation is similar to that of interactive causality. Interactive causal relationships have important clinical implications. In many cases, treatment effects can be enhanced by manipulating interactive variables that operate through the same causal path as another causal variable. Examining the paths of interactive causal variables can also assist the clinician in identifying causal mechanisms. Vulnerability refers to the probability that a particular behavior disorder will be exhibited as a function of a specific parameter of a causal variable, or to the magnitude of a causal variable necessary to trigger a specified behavior problem. Most vulnerability factors are relatively enduring characteristics of a person.

However,

their effects are often modifiable

through the introduction

of

mediating variables that operate through the same causal path. The best known examples of vulnerability models are those that invoke a diathesis-stress model in psychopathology. The concepts of vulnerability, mediating variables, latent causal variables, and original causal variables overlap.

Section II]

DYNAMIC, NONLINEAR, AND DISCONTINUOUS CAUSAL RELATIONSHIPS

Sa

oa

a

-

fl

rbenn nee
ite

a

@) eahhird

: Sem ibe = ‘

=

hef A t E ra Saale i

.

asl

v1 Oras wd

:

'

"

Ay

‘ ne

-

|

=

ry af if

¥7

Derr

;

cs

oto 5.

Ze tey se a - < 08E $F an i +

64=

f

17

1 :

-_

eipa

a

qQlaGA

aah!i:

hes

reser

Bet

”

FAN

wu

P vc

WWD

ry

1H

eles Paty ORK OT Veehetep

7 Soi

,

Me

Ss

;

!

a

oe

&

Mew

Pee es

a

iets & =

owe|

he wor

Jedi Be feel hdabow hsgece ee

"

shears ha iboegi i

=e

4

;

—

7

i

oo

ae =


ue

fact Sn meh idhoeabin

nie Peet

eta:

a eA e

7 =a emt

~ i

i)

a

teh) i LP ae © bs Mind la

.

ae

Titi, & i):

ath, teupon Seg o&

ites (ane Wool Seer sier fale yal oanwe a tow Mtl Ry sn

omalj So ever Suh

~~

cen

(ic aor

BO aPwti.i syn

ur

7—