Causal Diagrams in Psychopathology:

Applications in Models of Causality and Clinical

Decision-Making

Stephen N. Haynes

Stephen N. HaynesUniversity of Hawai’i, USA

University of Málaga, Spainsneil@hawaii.edu

IV Workshop on Causal Reasoning in Clinical Decision Making

April, 2009

Gracias a

Pedro L. Cobos et OtrosAntonio Godoy

• A presentation about

– A challenging context for clinical decisions:

• Understanding complex clinical cases in psychopathology

• Making appropriate treatment decisions with complex cases

• Communicating case formulations to others

– Application of Causal Diagrams in Psychopathology to Aid Causal Reasoning and Clinical Decisions

• Idiographic (variance within a person across time) -- the main focus!

• Nomothetic (variance across persons)

• Not about what I know, investigate, or teach

• About what I’m thinking about and learning

My Main Points?

• Causal diagrams – Help explain complex behavior

problems– Help in functional analysis and

Clinical Case Formulation– Provide an alternative, more precise,

clinically useful language in causal models of psychopathology and in clinical case formulation (CCF)

• Causal Diagrams:– Help estimate relative magnitude of

effect of various treatment foci, given component causal judgments

– Encourage Parsimony in causal models in psychopathology and CCF

– Help detect less obvious functional relations in psychopathology and treatment

– Guide focus of clinical research

• Causal Diagrams:

– Help evaluate between-clinician agreement in CCF judgments

– Help examine congruence between treatment mechanisms and causal relations for a client

• Causal Diagrams:

– Encourage a systematic evaluation by the clinician of component his/her component clinical decisions and judgments

– Help educate about clinical decisions, psychopathology, CCF (supervision, graduate training, other professionals)

• Causal Diagrams

– Emphasize importance of

• Multiple causal paths

• Bidirectional causal relations

• Moderator Variables

• Mediator Variables

– In psychopathology and clinical case formulation

• One goal of presentation:

– to promote a standardized, formalized, quantitative use of causal diagrams in psychopathology and clinical case formulation

– Discuss limitations, challenges in use of causal diagrams

More on Causal Diagrams

• Some examples of causal diagrams-->(some Idiographic (one person, across

time), some nomothetic (across persons)

Just note--elements and structures of diagrams

Example of a CBCF

Others laughed at or ridiculed me/didnot appreciate my work.

Talked or thought of talkingto others about my feelings.

Was reminded aboutlimitations of my job

Was reminded about limitations of living in ___

Situational triggersInternal and external

Idiosyncratic cognitiveschema

Distress

Worthlessness …

Inadequate/incompetent/trapped/hopeless job

Others hurting/rejectingand can’t be trusted

Anger inhibition: “Gentle-men don’t get angry.”

Depression

Concentration difficulties; worry

Anger

-

Cognitive CCF Causal Diagram for Depressed Client; Mumma

Armed conflicts in Nicaragua

www.american.edu/TED/ice/nicaragua.htm

Today’s Distress Item Parcels

.75***.75*** .75***

GD:Mixed

.79***.79*** .79*** .87*** .87*** .87***

Anger

ICS 1 Worthlessnes

s

ICS 3 Job: Inadequate

Trapped

ICS 2.1/3 Others Hurtful,

Rejecting

ICS 2.2Anger/Inhibition

.83***.83*** -.08-.08

.79***.79*** -.07 -.45**-.07 -.45**

.69***.69***.01.01

.28*.28*

.59*** .60*** .92*** .57*** .82*** .88*** .98*** .88***

.82*** .84***

Today’s Cognition Item Parcels

dd ee ff aa bb cc gg hh ii

GD:Depressed

a b i h j d c e f g

-.26***-.26*** -.19*-.19*-.28*-.28* -.20**-.20**

Χ2(df = 531) = 815.19, RMSEA = .083, CFI = .89.CCF causal diagram for depressed mood

JEFFREY'S

OPPOSITIONAL/DE

FIANT DISRUPTIVE

BEHAVIOR

INCONSISTENT

CONTINGENCIES

DEFICITS IN

PARENTING

SKILLS

ESCAPE AND

AVOIDANCE OF

UNPLEASANT

ACTIVITIESMRS. JORDAN'S

MOOD

FLUCTUATION

MARITAL

DIFFICULTIES

INTERMITTENT

REINFORCEMENT

SOCIAL SKILLS

DEFICITS

JEFFREY'S

AGGRESSIVE

BEHAVIOR

REQUESTS TO

PERFORM

UNWANTED

ACTIVITY

PEER

REINFORCEMENT

MARITAL

COMMUNICATION

DIFFICULTIES

DISRUPTIVE

CLASSROOM

9 yr-old boy; home and classroom problemsCCF Causal Diagram for Oppositional/Defiant Behs

Problem l

Problem 2

Problem 3

Problem l

Problem 2

MaintainingContingency

Problem l

Problem 2

Antecedent Causal

Variable

A

B

C

Problem 1

Problem 2

CausalVariable l

CausalVariable 2

FOUR TYPES OF FUNCTIONAL RELATIONS THAT CAN RESULT IN MULTIPLE BEHAVIOR PROBLEMS FOR A CLIENT

D

Schematic Causal Diagram for comorbidity

An example of Functional Analytic Clinical Case Model

WEEK END VISITS TO THE FAMILY

EXCESSIVE AND REPETITIVE PETITIONS TO STAFF

VERBAL AGRESSIONS TOWARD STAFF

PROPERTY DESTRUCTION

STAFF ATTENTION

RELAXATIONS

PRN MEDICATION

AROUSING EFFECTSOF COFFE

CCF Causal Diagram for verbal aggression

Ahn, Kim et al., 2005

Not complying with adult requests

Whining, grumbling (at

home)

Inattentive and off-task behavior

(at school)

unspecific commands

family disorganiz

ation

Genetic/Family history

of ADHD

Incomplete school work

deficits in parenting

skills inconsistent reinforce-

ments

child poor commun.

skills

child inattention

child unprepare

d for school

high neg. comments/ low pos. comments

varied sleep

schedule

CCF Causal Diagram child oppositional/attentional probs

William James

From William James: Talk to Teachers. 1899; Causal Model for Human Behavior

Leonardo da Vinci

The Da Vinci Notebooks at sacred- texts.com (Causal model proving that there is water on the moon)

Common Elements and Characteristics of Causal Diagrams Across Disciplines

• Illustrate complex functional relations among multiple variables (sometimes causal)– Input variables (causes of behavior

problems)– Output variables (Behavior problems

• Illustrate possible causal variables• Some show strength of relation• Most show direction of causal relation• Multiple formats for diagrams

What is necessary in causal diagrams in Psychopathology and

Functional Analysis?• Standardized presentation of elements

of psychopathology • Emphasis on Important elements

relevant to the explanation of the behavior problems and clinical decisions

• Amenability to quantification, to model effects of clinical decisions

Causal Diagrams inFunctional Analytic Clinical Case

Diagrams (FACCD)

• FA Focus on “important, modifiable causal variables and functional relations relevant to a person’s behavior problems”

• FACCD =

– Causal diagram for a functional analysis

– a subset of standardized path and causal diagrams; often used in physics, agronomy, economics, oceanography, (informally in mind-mapping).

Goal:

• To help clinician decide where for focus treatment (which causal variables should be the focus of treatment)

18 Elements In, and Outcomes from, a FACCD

(Functional Analytic Clinical Case Diagram)

(18 clinical judgments; All Leading to Estimates of the Relative Magnitude of Effect of Various Treatment Foci For an

Individual Client; but there are more factors affecting these decisions)

• 1. Multiple client behavior problems

• Note: Methods of deriving these judgments are discussed in references at end

• Note: variables are abstractions. Consider Ys = panic episodes, alcohol overuse, nightmares, aggressive behaviors, social anxiety, manic episodes, self-injury,etc.

Y1

Y2

Y3

2 Relative “Importance” of behavior problems. Estimated by:

– Risk of harm to self

– Risk of harm to others

– Personal distress

– Qualitative ratings of importance (by client, therapist, others)

– Note: relevant to estimating magnitude of effect of intervention

Y1

Y2

Y3

More Important

Less Important

3 Forms of functional relations between behavior problems

Y1

Y2

Y3

Correlated Non-causal

Unidirectional Causal

Bidirectional Causal

4. Strength of functional relations among behavior problems– Degree of “Influence”– Conditional probability of

occurrence– Time-lagged correlation– Estimated strength of manipulation

effects

Y1

Y2

Y3

Stronger

Weaker

5. Consequences of behavior problems

– Health risks

– Functional impairment

– Economic, legal risks

– Effects on others

– (e.g., risks associated with financial decisions during manic periods, legal risks associated with drug use)

Y1

Y2

Y3

Z1

• Note effect on treatment decisions for just a few elements of FACCD!

• If importance, form, direction were different, different focus could be indicated to achieve maximum magnitude of benefit for this client

6. Causal Variables– Broadly defined: empirically supported

variables where changes lead to changes in behavior problem ( or other variables)

– E.g., positive response contingencies, antecedent stimuli, avoidance, settings/contexts, negative ruminations, elevated adrenal responses, conditional emotional responses, outcome expectations, reduction in aversive states, life stressors, neurotransmitter receptor density, communication skills, etc

Y1

Y2

Y3

Z1

X1

X2

X3

X4

7. Modifiability of causal variables– “clinical utility” – relatively unmodifiable: brain injury, early

traumatic life experiences; genetic vulnerability, early learning)

– no effective treatments (e.g., some medical disorders, some neurological deficits)

– External factors--cooperation from partner, staff cooperation, unavoidable life stressors)

– Client factors--cognitive abilities, treatment adherence - interference

More Modifiable

Less Modifiable

Unmodifiable

Y1

Y2

Y3

Z1

X1

X2

X3

X4

8. Forms of functional relations between causal variables and behavior problems

Correlated Unidirectional Bidirectional Noncausal Causal Causal

Y1

Y2

Y3

Z1

X1

X2

X3

X4

9. Strength of Functional/Causal Relations between causal variables and behavior problems

Stronger

Weaker

Y1

Y2

Y3

Z1

X1

X2

X3

X4X4

• 10-11 Form and Strength of causal relations among causal variables

Y1

Y2

Y3

Z1

X1

X2

X3

X4X4

12-16: Additional Types of Causal Variable and Causal Relations:

– Moderating variable

– Mediating variable

– Hypothetical causal variable

– Interactive causal relations

– Causal chains

Moderating Variable

Mediating Variable

Hypothetical Causal Variable

Causal Chain

Interactive Causal

Moderating variable (affects the strength of relationship between two other variables; Can be buffering, protective, etc.)

Y1

Y2

Y3

Z1

X1

X2

X3

X4X4

X5

Mediating Variables: “Explain/account for” the relations between two other variables (e.g., why/how does X1 ---> Y?)

X1

X2

X3 Y

Y1

Y2

Y3

Z1

X1

X2

X3

X4X4

X5

Hypothetical causal variable and relationship (not measured, inferred, to be measured, indicated in Nomothetic research)

Hypothetical Causal Variable

Hypothetical Causal Relation

Y1

Y2

Y3

Z1

X1

X2

X3

X4X4

X5

X6

Causal chains (distal/proximal)

Can be “Mediated” causal variable/relation also (one that “explains” a causal relation)

X1 X2 X3 Y1

Y1

Y2

Y3

Z1

X1

X2

X3

X4X4

X5

X6

X7

17. Direction of Functional Relations

Y1

Y2

Y3

Z1

X1

X2

X3

X4X4

X5

X6

X7

( X1 = X2)-

18. Temporal Relations Among Variables

• Temporal order flows from left to right, with earlier events to the left of later events. Thus, in the next figure, variable X7 occurs before variable X3

• Note the erroneous causal inferences that might result

Y3

X3

X7Y2

MARITAL DISTRESS

(60)

ATTENTION FROM H (.8)

WARM-

CUTTING(90)

WDEPRESSION

(40)

MANY SOURCES OF

DISAGREEMENT(.2)

POOR COMMUNICATION

SKILLS (.8)

EXCESSIVE ALCHOL INTAKE

BY H & W(40)

.8

.8

.8.5

VERBAL ABUSE BY H (.5)

ABUSIVE FAMILY

HISTORY OF W(0)

FEW POSITIVE INTERACTIONS(H & W)(.8)

FEW SOCIAL SUPPORTS FOR H & W

.8

.5

EXTERNAL STRESSORS (H

JOB)(.2)

NEGATIVEVERBAL

INTERACTIONS(.8)

.5

.2

.2

.5

.5

.5.2

H=HUSBAND; W=WIFE

.8

EARLY LEARNING:

SELF-INJURYW (0)

.8

.2

DISTRACTION, RELAXATION

.2

.8

SELF-INJURIOUS SELF-REFERRED OUTPATIENT ADULT CLIENT

ATTENTION FROM

PROFESSIONALS

2.

Benefits of Idiographic Causal Diagrams (FACCDs) for Clinical Decision-Making

• Can estimate treatment focus with greatest magnitude of effect for client

• Can indicate where additional assessment is needed

• Can indicate potentially important but unmeasured causal variables

Qualitative Implications, 2

• Emphasizes focus on important, modifiable variables

• Encourages parsimony in communicating FA to others

• Models potential effects of interventions: (similar to “modeling” approaches in physics, oceanography, economics, agronomy)

Qualitative Implications, 3• Indicates omitted variables and

functional relations: nomothetically based potential causal relations that are not operational for a particular client

• Has “face value” for other professionals– (They can understand why you want to

pursue a particular intervention strategy)

• Emphasizes importance of good quality assessment for the most valid judgments

Qualitative Implications, 4• Can be used in a constructive, positive

manner. FACCDs can focus on client goals, values, strengths.

• Mandates knowledge of treatment mechanisms for various treatments which, in turn, can guide assessment efforts

• It promotes a logical, sequential, linear approach to clinical case formulation.

Qualitative Implications, 5

• It requires that the clinician examine his or her individual clinical judgments (e.g., does this patient’s marital conflict strongly affect his use of alcohol)

• Can suggest possible outcomes if natural changes occur in patients’ environment, behavior, thoughts, emotions

***Quantification of Causal Diagrams***:

Assigning Quantitative Values to the Elements of an

Idiographic Causal Diagram(Functional Analytic Clinical

Case Diagram)

• Sample Weights:• Importance of behavior problems

– 1= mild– 2=moderate– 4=severe

• Strength of functional relations– .2 =weak – .4 =moderate– .8 =strong

• Modifiability of Causal variable – .2 =mild – .4 =moderate– .8 = strong

Y1

Y2

Y3

X1

X2

X3

(1)

(2)

(4)

.2

.4

.8

.2

.4

.8

X40

• Absolute values have no effect on clinical judgments derived from quantitative analyses within the FACCD

• Only relative values (ratios) within a FACCD affect the judgments (as long as they are linear transformations), – E.g., for modifiability (.2 - .8) = (.1 - .4)

• (except for Sensitivity Analysis, discussed later)

• And, more face value if they approximate expected true functional relations and values (importance, modifiability)

Assets of Causal Diagram Quantification 1

Estimating Relative Magnitude of Effect in FACCD

• Allows the calculation of vector coefficients, to model intervention judgments

• Relative (within-person) Magnitude of Effect (ME) of one causal variable:

• ME(xi/yi) = ∑(xi/yi) (sum of all path coefficients from xi)

Y1

Y2

1

X1

.8

X2

.2

.2

.8

.8

.2

3

ME(x1) = (.8 x .2 x 3) + (.8 x .2 x .2 x 1) = .51

ME(x2) = (.2 x .8 x 3) +(.2 x .8 x .2 x ) +

(2. X .8 x 1) = .67

25% increase in expected ME for X2

Y1

Y2

Y3

Z1

X1

X2

X3

X4X4

X5

X6

X5

ab

c

de

f

gh

i

Y1

3

Y1

Y3

Z1

X1

.2

X2

.8

X3

X4X4

X5

.8

X6

X5

.2.2

.8

.2.2

.04

.8.8

.2

• Note 3 causal paths from X1 --> Y1 (some moderated and mediated

ME(x1/y1) =

Direct Path = (.2 x .2 x 3) +

Through X2 = (.2 x .2 x .8 x .2 x 3) +

Interaction Path (with X2) = (.2 x .2 x 8 x .2 x 3)

(.12) + (.02) + (.02) = .16 (relative magnitude of effect)

Can then compare to other Path Coefficients for other causal variables

• MEs can be estimated by analyzing graphical properties of the diagram (values) or performing symbolic derivations governed by the diagram (using algebra symbols) (Pearls, 1995)

• A benefit of quantification of causal diagrams (FACCDs): causal variables with multiple causal paths are particularly important in accounting for and modifying behavior problems

• Can estimate the likely effects of planned and unplanned changes in causal variables, or the introduction of new moderator, mediator variables. “What would happen if…?”

Adding “Uncertainty”

• The parameters of the elements in causal models are only estimates based in the best available evidence.

• Can establish domains of confidence in the outcome of our predictions and interventions

• Reflects measurement and judgment limitations• In addition to “error” components in all causal

diagrams (referring to unmeasured causal variables)

• Uncertainty Analysis– indicates the degree of uncertainty in the

overall magnitude of effect associated with a causal variable (reflects the cumulative uncertainty in the causal model)

– draws attention to variables about which more information is needed to reduce their degree of uncertainty and increase confidence in the causal model

• Uncertainty tolerance– the degree of uncertainty that can be tolerated

depends on the importance of the judgments– Whenever important judgments are being

made, or when the negative consequences of an erroneous decision are severe, such as use of an invasive treatment with a patient, additional data should be acquired to reduce uncertainty

Y1

Y2

1

X1

(.6-.8)

X2

(.2-.6)

(.2-.3)

(.6-8)

(.5.8)

(.2-.6)

3

ME(x1) = ((.6-.8) x (.2-.3) x 3) + ((.6-.8) x (.2-.3) x (.2-.6) x 1) = .37-.76

ME(x2) = ((.2-.6) x (.6-.8) x 3) +((.2-.6) x (.6-.8) x (.2-.6) x 1) + ((2.-.6) X (.5-.8) x 1) =

.48-1.72 (compared to .67 assuming no uncertainty)

Uncertainty Analysis (adding confidence limits to parameter estimates)

Increasing Acceptability of Causal Diagrams: “Causal

Relevance Diagrams”• When quantitative information is

encoded in the elements (path coefficients, importance and modifiability ratings) of a causal diagram (Shafer, 1996)

Y1

Y2

1

X1

.8

X2

.2

.2

.8

.8

.2

3

Given a Legend, this causal path diagram is the same as…

Y1

Y2

X1

X2

This causal relevance diagram.

Sometimes more clinically acceptable and computationally identical

Assets of Causal Diagram Quantification 3:

Illustrating the effects of changes in clinical judgments on MEs and optimal treatment

foci

Y1 (40)

Y2 (80)

Y3 (20)

X1(.4)

X2(.2)

X3(.8)

.

.5..33.3��.4

,5

M1(.8)

.8

X = CAUSAL VARIABLEY = BEHAVIOR PROBLEMM = MEDIATING VARIABLE

() = RELATIVE IMPORTANCE OR MODIFIABILITY

X4(.4)

.2

.8

.4

.4

.4

CLINICAL CASE MODEL (FACM) #1

X3 = (.8 * .8 * 20) +(.8 *.8 * .4 * 80) = 33.28 (RELATIVE UNITS)

.2

Moderating

Y1 (40)

Y2 (80)

Y3 (20)

X1(.4)

X2(.8)

X3(.2)

.

.5..33.3��.4

,5

M1(.8)

.8

X = CAUSAL VARIABLEY = BEHAVIOR PROBLEMM = MEDIATING VARIABLE

() = RELATIVE IMPORTANCE OR MODIFIABILITY

X4(.4)

.2

.8

.4

.4

.4

CLINICAL CASE MODEL (FACM) #2 CLINICAL CASE MODEL (FACM) #2

(Changing Clinical Utility)(Changing Clinical Utility)

X3 = (.2 * .8 * 20) +(.2 *.8 * .4 * 80) = 8.32 (RELATIVE UNITS)

X2 = (.8 * .4 * 80) = 25.6

.2

NOTE MULTIPLE CAUSAL PATHS FROM X1 TO Y2

.2

Moderating

Y1 (40)

Y2 (20)

Y3 (80)

X1(.4)

X2(.8)

X3(0)

.

.5..33.3��.4

,5

M1(.8)

.8

X = CAUSAL VARIABLEY = BEHAVIOR PROBLEMM = MEDIATING VARIABLE

() = RELATIVE IMPORTANCE OR MODIFIABILITY

X4(.4)

.2

.8

.4

.4

.4

CLINICAL CASE MODEL (FACM) #3CLINICAL CASE MODEL (FACM) #3

CHANGING IMPORTANCE AND CLINICAL UTILITYCHANGING IMPORTANCE AND CLINICAL UTILITY

M1 = (.8 * .8 * .8 * 80) +(.8 * .8 * .8 * .4 * 20) = 45.05

.2

NOTE MULTIPLE CAUSAL PATHS FROM X1 TO Y2

.2

Moderating

• Another benefit of quantification of causal diagrams (FACCDs): Makes the clinician question his/her judgments e.g.:– Are the communication problems of a

distressed couple twice as modifiable as their negative attributions about each other?

– Is a client’s depressive episodes twice as important as their overuse of alcohol?

Assets and Implications of Model Quantification 4

• Assuming a content valid FACCD all appropriately focused interventions will be effective (compared to no intervention), but with differential magnitudes of effect – Some supporting literature on increased

ME with FACCD-focused treatments; only from SIBs

Assets and Implications of Model Quantification 4

• The Effects of Treatments With Multiple Mechanisms/Components

• Examples of treatment mechanisms/components– Automatic negative self-statements in treatment of

depression– Identifications of emotions with anxiety disorders– Communication training with distressed couple– Alcohol outcome expectancies– Guilt in sex-abuse (or assault) related PTSD– Parental use of + reinforcement with child– Experiential avoidance– Desensitization in social anxiety reduction

Model 1:A Good Match Between the Functional Analysis and the

Treatment

Client 1BehaviorProblem

1

CausalVariable

2

Causal Variable

3

Causal Variable

4

CausalVariable

1

Treatment 1

TreatmentMechanism

1

TreatmentMechanism

2

TreatmentMechanism

3

Estimating Magnitudes of Effects

• Assigning relative values to variables and paths– Behavior problem importance: 10– Strong causal/functional relations: .8– Weak causal/functional relations: .2– Modifiability of causal variables: .8

(note; the strength, modifiability and estimates are only judgments by the clinician, informed by the outcome of assessment)

Solving for Paths for client 1:

• Magnitude of Effect of this treatment, for this client, given these clinical judgments, is 10.4

• (Note: value has no absolute meaning and is useful only for comparing effects of different clinical judgments using the same values for a client)

Model 2:A Less-Than-Optimal Match

Between the Functional Analysis and the Treatment

Client 2BehaviorProblem

1

CausalVariable

2

Causal Variable

3

Causal Variable

4

CausalVariable

1

Treatment 1

TreatmentMechanism

1

TreatmentMechanism

2

TreatmentMechanism

3

Changes in Functional Analysis From 1st client

• Different client--> Same behavior problem, causal variables, treatments, and treatment mechanisms.

• Only the strength of causal relations have been changed-

• Now: Major treatment mechanisms are relevant but do not match (are less congruent with) the most important causal relations

Solving for Paths for client 2

• Magnitude of Effect of this treatment, for this client, given these clinical judgments, is 5.1

• About 1/2 of magnitude of effect for client 1

Model 3:The Magnitude of Effects for Two More Narrowly focused

Treatment

Client 1BehaviorProblem

1

CausalVariable

2

Causal Variable

3

Causal Variable

4

CausalVariable

1

Treatment 2

TreatmentMechanism

Treatment 3

TreatmentMechanism

Changes in Functional Analysis From Previous

clients• Same client, causal variables, causal

relations• Two treatments, each more narrowly

focused (fewer treatment mechanisms)• Treatment 2 addresses strong causal

relation• Treatment 3 addresses weak causal

relation

Solving for Both Paths for client; Magnitudes of Treatment Effects:

• Treatment 2: 5.1

• Treatment 3: 1.3

Model 4:The Magnitude of Effects for a

Broadly focused Treatment

Client 1BehaviorProblem

1

CausalVariable

2

Causal Variable

3

Causal Variable

4

CausalVariable

1

Treatment 4

TreatmentMechanism

1

TreatmentMechanism

2

TreatmentMechanism

3

Solving Paths for Broadly Focused Treatment

• This treatment is the most effective because it addresses all causal variables

• Magnitude of effect is 13.4--20% greater than best of more focused treatments

Implications• All validated treatment programs will be

effective if they address any causal variable for a client (noted in “3”), given stability of other factors

• Relative Magnitudes of treatment effects will be affected by match between treatment mechanisms and causal variables operating for an individual treatment

Implications 2

• Idiographic treatment (a treatment designed to match the clinical case formulation for the client) will often be more effective than standardized treatments

Implications 3

• Magnitude of treatment effect will be affected by match (congruence) between treatment mechanisms and causal variables operating for an individual client

Implications 4• incremental treatment effectiveness for

individualized treatment is affected by:– The degree to which causes for a behavior

problem differ across clients (in causal variables and the strength of relationships)

• Implications for treatment research– Measure causal relations for client– Look for match between causal relations and

treatment mechanisms– Group comparisons without examining treatment

mechanisms and treatment-causal variable match are not optimally useful

Implications 5

• The identification of potential treatments for a behavior problem can guide assessment foci (Godoy)

• e.g. if insomnia is problem: clinician should assess presleep thoughts, presleep physiological arousal, sleep hygiene, stimulus control factors (the mechanisms thought to underlie different treatments)

Assets and Implications of Model Quantification 5

• Causal Diagrams and Calculating Inter-Clinician Agreement for FA and CCF

• Allows for a more refined analysis of specific areas of agreement and disagreement– Agreement about client problems– Agreement about causal variables– Agreement about functional relations

• Five possible agreements (omitting “modifiability” for this example) – one for each of the three causal variables (B, C,

D) identified by both raters– one for causal variable A (identified by

clinician B)– one for causal variable E (identified by

clinician A)

• A sample quantification of the degree of agreement: – “0” for no agreement or congruence

– “1” for weak agreement or congruence (e.g., Causal Variable C identified by both raters who disagree about its strength of relation with the BP

– “2” for strong agreement (e.g., Causal variable B; variable and strength of relation).

• The sum of these agreements 4 out of 10 (40%)– A=0

– B=2

– C=1

– D=1

– E=0

• Can examine overlap of 2 full causal diagrams: see Tufts University site

• Complication 1: Semantic Similarity• Complication 2: Relative strength vs true

agreement of functional relations

Assets and Implications of Model Quantification 6:

Intervention with Proximal vs Distal Causal Variables

• Intervention with proximal, compared to distal, causal variables will always have greater but often less generalizable magnitude of effects (assuming strength of causal relations are the same)

David Hume, 1740

“The idea, then, of causation must be derived from some relation among objects. . . whatever objects are considered as causes or effects, are contiguous; and that nothing can operate in a time or place, which is ever so little removed from those of its existence. Though distant objects may sometimes seem productive of each other, they are commonly found upon examination to be linked by a chain of contiguous causes”

Distal and Proximal Causal Variables

X1 proximal

X3

X2Distal

X4

Y2

Y1

Y3

• Similar to Aristotle’s concept of

Final cause: That cause but for which a thing would not exist; the final purpose of a thing.

• Relevant for “systems-level functional analysis” (e.g., institutional systems issues affecting aggression in psychiatric units)

• Relevant to some “personality-based” treatments, with important referral problems– Generalized fears of rejection/abandonment, paranoid

ideation, irritability, when main target is conflict with partner

– E.g., X1 = escalation in marital conflict X2 = early learned fears of abandonment Y1 = marital distress Y2, Y3 = other relationship distress

• Effect of X2 on Y1 will always be less than effect of X1 on Y1 because the ME of X2 is modified by path and modifiability coefficients associated with X1

• Relative difference in ME of X1 and X2 will be a function of values for X3 and X4 (strength and modifiability), as well as X2 --> X1 paths– Therefore, ME of X2 can be greater than X1, given

greater importance of generalized effects

• Should you focus on immediate vs distal (general) causal factors? Amenable to quantifiable analysis. A function of:– Relative importance of behavior problem

that is down stream from proximal causal variable

– Relative strength of relations• Distal ---> multiple behavior problems• Proximal ---> main behavior problem

• Generalized vs specific causal variable, considerX1 = escalation in marital conflict

X2 = early learned fears of abandonment, or paranoid tendencies, or fears of rejection, or critical interpersonal style

Y1 = marital distress

Y2, Y3 = other relationship distress

Distal and Proximal Causal Variables

X1 proximal

X3

X2Distal

X4

Y2

Y1

Y3Probably, bidirectional relations between causal variables, with treatment

Assets and Implications of Model Quantification 7:

An emphasis onBidirectional Causal Relations

• Useful in treatment foci– Treatment can focus on either variable– Beneficial effects can continue after

treatment termination--reverberation

• e.g., marital distress <---> depression paranoid thoughts <---> social

isolation

Y1X1

PRESLEEP

WORRY

OUTCOME

EXPECTANCIES

SOCIAL

ISOLATION

SLEEP-ONSET

INSOMNIA

ALCOHOL/DRUG

USE; EXERCISE

RATE; MOTOR

SKILLS; SOCIAL

PERFORMANCE

PARANOID

IDEATION

CHILD

TANTRUMS

PARENTAL

REINFORCEMENT

RECIPROCAL CAUSAL RELATIONSHIPS

Y1

Behavior Problem

100

X1

Causal Variable

(.4)

Y2

Behavior Problem

100

X3

Causal Variable

(.4)

X2

Causal Variable

(.8)

a b c

a b c d e

Example A

Example B

Example A

For impact of X1 = abc + bab(c-abc)

Example B

For impact of X3 = cde + cbabcd(e-cde)

b, d = coefficient of covariance (r, R)

.5 .5

.5

Principles

l.calculate all unidirectional paths first

2.calculate "remainder" of behavior

problem importance, to use for

bidirecitonal calculations

3.one pass only on bidirectional path

(unless reverberations is feasable,

continue calculating remainders??)

4.note that impact of X3 will always be

larger than impact of X2

Y1 (40)

Y2 (80)

X1

(.4)

X2

(.2)

.5..33.3��

.4

X = CAUSAL VARIABLE

Y = BEHAVIOR PROBLEM

M = MODERATING VARIABLE

() = RELATIVE IMPORTANCE OR MODIFIABILITY

X4

(.4).8

.4

RECIPROCAL CAUSATION

.2

.8

1.EFFECT OF X1 ON Y1 WILL ALWAYS BE GREATER THAN EFFECT OF X4 ON Y1

2.X4 CAN EFFECT Y2 THROUGH X1 AND X2,

3.REVEBERATION BETWEEN X1 AND Y1, WILL AFFECT Y2 THROUGH X2

4. SMALL CHANGES IN ESTIMATED COEFFICIENTS WILL STRONGLY

EFFECT MAGNITUDE OF EFFECT

5.DIMINISHING EFFECTS WILL OCCUR AFTER INITIAL CHANGE IN X1

??

• Note relevance of differential calculus– Each slope approaches a maximum (where

slope, first derivative, approximates 0)– So, can estimate degree to which a focus

on a bidirectional relation results in greater magnitude of effect than a focus on a unidirectional causal relation by comparing first derivative with causal path of unidirectional causal relation

• Another benefit of quantification of causal diagrams (FACCDs): Sensitivity analysis– how the variation in the output of a mathematical

model can be apportioned, qualitatively or quantitatively, to different sources of variation in the input of a model

– what proportion of variance in behavior problems does the causal model (the clinician think) can be controlled?)

– Requires that functional relations affecting a BP assume true “proportions of variance” (different than the relativistic approach discussed thus far)

• Limitations of Causal Diagrams and FACCD (Idiographic causal models)– Validity limited by validity of

assessment data; often limited assessment strategies

– Are “hypothesized” “clinical judgments” and reflect biases of clinician

– Causal relations can be unstable across time, validity is time-limited

– Dynamic aspects of behavior problems are not modeled• Bipolar shifts• Borderline personality disorders --

emotional lability (the dimension of interest)

• Latency of causal effects (arrows not proportional to latency of causal effects; are ordinal, rather than ratio in terms of strength)

– Consider problem of selecting weaker but quicker causal relation vs. stronger but delayed causal relation?

– Semantic ambiguity overlap/imprecision in variables

• Consider “different”? causal variables such as

–Marital conflict - marital distress–Reinforcement - response contingency–Contextual- setting - antecedent factors–Arousal - Physiological arousal-

tension,–Monitoring deficits - inattention,

attention deficit

–Pseudo-precision: Quantification increases the patina of precision (because of measurement/inferential limitations)

–Better to use “ß” (reflecting units of change, rather than “R”?)

• FACCD’s (+ some nomothetic causal diagrams are overidentified)

• >100% of variance accounted for in behavior problem– Causal variables necessarily

related/dependent– Semantic overlap?– Different response modes (activity,

thinking, physiological)– But, can still search for greatest ME

–Incomplete--Do not contain all causal variables; often omit variables between a cause and a behavior problem

–Nonlinear functional relations• Many medication effects• Pleasant/unpleasant events -->

mood (functional plateaus)• Physiological arousal --> cognitive

functioning• Life stressors --> psychological

symptoms (catastrophic functions) use of algebraic functions in

paths? Or unnecessary?

Χ Χ Χ

Χ Χ

Χ Χ

Χ

Χ

Χ

Χ

Χ

Χ Χ

Χ Χ

Χ Χ Χ

O

O

O

O O

O O O O O O O O O O O O O O∆ ∆ ∆ ∆ ∆ ∆

∆

∆

∆

∆

∆

∆

∆

∆

∆ ∆ ∆ ∆ ∆

0

1

2

3

4

5

6

7

8

9

10

Χ Person A

Person B

O Person C

∆ Person D

Samples Across Time

BLOOD PRESSUREMARITAL SATISFACTIONDAILY STRESSORSMEDICATION ADHERENCEMOODSELF-INJURY

Y1

Y2

X1

X2

Modeling nonlinear functional relations?

a: y = aX23 (parabolic function between

X and Y)

a

References and Sources• sneil@hawaii.edu (e-mail for copies of the PP

presentation)

• Website with behavioral assessment definitions, clinical case examples of FACCDs, manuscripts.

http://www2.hawaii.edu/~sneil/ba/Login: behavioral; Password: assessment

• For visual graphics, diagramming software:http://vue.tufts.edu/

• Causation, Prediction, and Search, 2nd Edition, (2001), by P. Spirtes, C. Glymour, and R. Scheines ( MIT Press)

• Causality: Models, Reasoning, and Inference, (2000), Judea Pearl, Cambridge Univ. Press

• Computation, Causation, & Discovery (1999), edited by C. Glymour and G. Cooper, MIT Press

• Causality in Crisis?, (1997) V. McKim and S. Turner (eds.), Univ. of Notre Dame Press.

• The Art of Causal Conjecture (1996). Glenn Shafer. MIT Press.

• Haynes, S. N. and O’Brien. W. O. (2000). Principles of Behavioral Assessment: A Functional Approach to Psychological Assessment. New York: Plenum/Kluwer Press..

• Haynes, S. N. & O'Brien, W. O. (1990). The functional analysis in behavior therapy. Clinical Psychology Review, 10, 649-668.

• Haynes, S. N., Uchigakiuchi, P., Meyer, K., Orimoto, Blaine, D., and O’Brien, W. O. (l993). Functional analytic causal models and the design of treatment programs: Concepts and clinical applications with childhood behavior problems. European Journal of Psychological Assessment, 9, l89-205.

• O’Brien, W. H. & Haynes, S. N. (1995). A functional analytic approach to the conceptualization, assessment and treatment of a child with frequent migraine headaches. In Session., l, 65-80.

• Haynes, S. N., Richard, D., & O’Brien, W. B. (l996).The Functional Analysis in Behavior Therapy: Estimating the Strength of Causal Relationships for the Design of Treatment Programs. Gedrags-therapie, 4, 289-3l4.

• O’Brien, S. N., & Haynes, S. N. (1997) Functional analysis. In: Gualberto Buela-Casal (Ed): Handbook of Psychological Assessment. Madrid: Sigma

• Floyd, F., Haynes, S. N., & Kelly, S. (1997). Marital assessment: A dynamic and Functional analytic Perspective. In: W. K. Halford, & H. J. Markman (Eds.). Clinical handbook of marriage and couples intervention (pp 349-378). New York: Guilford Press

• Nezu, A., Nezu, C., Friedman, & Haynes, S. N. Case formulation in behavior therapy. T. D. Eells (Ed.) (l997). Handbook of psychotherapy case formulation. NY: Guilford.

• Haynes, S. N., Leisen, M. B., & Blaine, D.D. (1997). Functional Analytic Clinical Case Models and Clinical Decision-Making. Psychological Assessment, 9, 334-348.

• Haynes, S.N. (1998). The assessment-treatment relationship and functional analysis in behavior therapy . European Journal of Psychological Assessment, 14 (1), pp. 26-34.

• Haynes, S. N., & Williams, A. W. (2003). Clinical case formulation and the design of treatment programs: Matching treatment mechanisms and causal relations for behavior problems in a functional analysis. European Journal of Psychological Assessment,19, 164-174.

• Haynes, S. N. (2005). La formulacion cliniaca conductual de caso: pasos para la elaboracion del analisis funcional [Behavioral clinical case formulation: guidelines on the construction of a functional analysis]. In V. E. Caballo (ed.), Manual para la evaluacion clinica de los trastornos psicologicos: Estrategias de evaluacion, problemas infantiles y trastornos de ansiedad [Handbook for the clinical assessment of psychological disorders: Assessment strategies, childhood problems and anxiety disorders] (pp. 77-97). Madrid: Piramide.

• Virus-Ortega, J., & Haynes, S. N. (2005). Functional analysis in behavior therapy: Behavioral foundations and clinical application. International Journal of Clinical and Health Psychology, 5, 567-587

• Haynes, S. N. & Kaholokula, J.K. (2007). Behavioral assessment. In: Hersen and A. M. Gross Handbook of Clinical Psychology John Wiley and Sons, New York.

• Raimo Lappalainen, R., Timonen, T, & Haynes, S. N. (2009). The functional analysis and functional analytic clinica case formulation--a case of anorexia nervosa. In P. Sturmey (Ed.). Clinical case formulation

• Kaholokula, J. K., Bello, I. Nacapoy, A. H., Haynes, S. (in press). Behavioral assessment and functional analysis. D. Richard & S. Huprich (Eds): Clinical Psychology: Assessment, Treatment, and Research

The End