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distribution. One of the great virtues of structural equation models is that they permit the quantification of causal and noncausal sources of statistical relationship. The present article discusses efficient matrix methods of computation for effect decomposition and extends these methods to models with unstandardized variables and to nonrecursive models. An appendix includes a computer program, written in APL, which implements the techniques described in the article.

In the last decade, there has been considerable progress in understanding a certain class of statistical models, known as directed acyclic graph (DAG) models, which encode independence, and conditional independence constraints. (See Pearl, 1988). This research has had fruitful results in many areas: there is now a relatively clear causal interpretation

this article should be addressed to Martin Zack. Centre for Addiction and Mental Health, Addiction Research Foundation Division. 33 Russell Street, Toronto, Ontario, Canada M5S 2S1. Electronic mall may be sent to mzack@aff. org. predictor of treatment outcome in substance abusers {McLellan, Childress, Ehrman, & O'Brien, 1986)) One way PD may undermine treatment outcome is by promoting relapse. It is well established that negative affect is the most common antecedent of relapse in problem drinkers (e.g., Litman, Stapleton, Oppenheim, Peleg, & Jackson, 1983: Marlatt & Gordon, 1985; Vuchinich & Tucker, 1996). Furthermore, negative affect is more likely to precede a major relapse than a minor one (Hodgins, el-Guebaly, '& Armstrong, 1995). Self-report indexes have shown that the intensity of negative affect is significantly higher in psychiatric outpatients with an alcohol use disorder than in those with no alcohol use disorder (Zack, Toneatto, & Streiner, 1998). This may reflect the summation or interaction of PD and situational negative affect in problem drinkers (McCusker & Brown, 1991). The resulting increase in the actual or perceived intensity or frequency of negative affect may increase the risk of relapse in high PD drinkers. In accord with this suggestion, alcoholics with a negative temperament, as measured by the General Temperament Survey (Clark & Watson, 1989), were significantly more likely to abuse alcohol in negative affectire states than were alcoholics with a positive temperament (Cannon, Leeka, Patterson, & Baker, 1990: Cannon et al., 1992). The correspondence between PD and drink- ing in negative affectire states, coupled with the strong association between negative affect and relapse, suggests that differential sensitivity to negative affect may predispos...

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Anxiety is associated with increased craving following in vivo cue exposure in alcoholics. Theoretical accounts [Psychol. Rev. 97 (1990) 147.] have proposed that conscious, deliberate cognitive processes underlie increased craving in drinkers who are trying to abstain. The present study tested the hypothesis that anxiety is associated with biases in explicit (i.e., conscious, deliberate) memory that promote recall of alcohol-related concepts in response to negative affective cues. Fiftytwo (seven females) outpatient problem drinkers performed a cued recall task that assessed memory for alcohol-related (ALC), negative affective (NEG), and neutral (NEU) target concepts that had been paired with NEG, ALC, and NEU cues during an incidental study phase. Higher anxiety was associated with increased recall of ALC targets paired with NEG cues. State and trait anxiety were intercorrelated, with higher levels coinciding with a higher frequency of drinking in negative mood states. These findings demonstrate a correspondence between anxiety and alcohol-related memory, and suggest that explicit memory biases may contribute to increased subjective responses (e.g., craving, expectancies) to alcohol stimuli in anxious problem drinkers. D 2002 Elsevier Science Ltd. All rights reserved.

For sale by the Superintendent of Documents, U.S. Government Printing Office Washington, D.C. 20402The NIDA Research Monograph series is prepared by the Division of Research of the National Institute on Drug Abuse. Its primary objective is to provide critical reviews of research problem areas and techniques, the content of state-of-the- art conferences, integrative research reviews and significant original research. Its dual publication emphasis is rapid and targeted dissemination to the scientific and professional community

Abstract. The discovery of causal relationships from empirical data is an important problem in machine learning. In this paper the attention is focused on the inference o fprobabilis tic causal relationships, for which two different approaches, namely Glymour et al.'s approach based on constraints on correlations and Pearl and Verma's approach based on conditional independencies, have been proposed. These methods differ both in the kind of constraints they consider while selecting a causal model and in the way they search the model which better fits to the sample data. Preliminary experiments show that they are complementary in several aspects. Moreover, the method of conditional independence can be easily extended to the case in which variables have a nominal or ordinal domain. In this case, symbohc learning algorithms can be exploited in order to derive the causal law from the causal model. 1

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A brief overview of current views of child development is provided, with particular attention given to the role the child's physical and social environment plays in influencing the developmental process. Examples from the recent literature are used to illustrate how these factors can influence lead exposure and most importantly how they might interact with lead to ameliorate or exacerbate possible lead effects. An example is provided which demonstrates that failure to control adequately and to adjust the data statistically to correct for the influence of these factors can lead one erroneously to attribute cognitive and behavioral changes to lead. Finally, data from the Cincinnati Prospective Lead Study are presented to illustrate the application of structural equation modeling as a means for unraveling the complex web of sociodemographic, environmental and behavioral influences on childhood lead exposure. The latter analysis indicates that for children less than 24 months of age, lead-containing dust in the home and on the children's hands are important determinates of their blood lead levels. This relationship is influenced by the amount of maternal involvement with their child and other indices of interaction between the child and primary caregiver.