From correlates to causes: can quasi-experimental studies and statistical innovations bring us closer to identifying the causes of antisocial behavior?

Longitudinal, epidemiological studies have identified robust risk factors for youth antisocial behavior, including harsh and coercive discipline, maltreatment, smoking during pregnancy, divorce, teen parenthood, peer deviance, parental psychopathology, and social disadvantage. Nevertheless, because this literature is largely based on observational studies, it remains unclear whether these risk factors have truly causal effects. Identifying causal risk factors for antisocial behavior would be informative for intervention efforts and for studies that test whether individuals are differentially susceptible to risk exposures. In this article, we identify the challenges to causal inference posed by observational studies and describe quasi-experimental methods and statistical innovations that may move researchers beyond discussions of risk factors to allow for stronger causal inference. We then review studies that used these methods, and we evaluate whether robust risk factors identified from observational studies are likely to play a causal role in the emergence and development of youth antisocial behavior. There is evidence of causal effects for most of the risk factors we review. However, these effects are typically smaller than those reported in observational studies, suggesting that familial confounding, social selection, and misidentification might also explain some of the association between risk exposures and antisocial behavior. For some risk factors (e.g., smoking during pregnancy, parent alcohol problems), the evidence is weak that they have environmentally mediated effects on youth antisocial behavior. We discuss the implications of these findings for intervention efforts to reduce antisocial behavior and for basic research on the etiology and course of antisocial behavior.     

Using Ensemble-Based Methods for Directly Estimating Causal Effects: An Investigation of Tree-Based G-Computation

Researchers are increasingly using observational or nonrandomized data to estimate causal treatment effects. Essential to the production of high-quality evidence is the ability to reduce or minimize the confounding that frequently occurs in observational studies. When using the potential outcome framework to define causal treatment effects, one requires the potential outcome under each possible treatment. However, only the outcome under the actual treatment received is observed, whereas the potential outcomes under the other treatments are considered missing data. Some authors have proposed that parametric regression models be used to estimate potential outcomes. In this study, we examined the use of ensemble-based methods (bagged regression trees, random forests, and boosted regression trees) to directly estimate average treatment effects by imputing potential outcomes. We used an extensive series of Monte Carlo simulations to estimate bias, variance, and mean squared error of treatment effects estimated using different ensemble methods. For comparative purposes, we compared the performance of these methods with inverse probability of treatment weighting using the propensity score when logistic regression or ensemble methods were used to estimate the propensity score. Using boosted regression trees of depth 3 or 4 to impute potential outcomes tended to result in estimates with bias equivalent to that of the best performing methods. Using an empirical case study, we compared inferences on the effect of in-hospital smoking cessation counseling on subsequent mortality in patients hospitalized with an acute myocardial infarction.     

Assessing moderated mediation in linear models requires fewer confounding assumptions than assessing mediation

It is well known from the mediation analysis literature that the identification of direct and indirect effects relies on strong no unmeasured confounding assumptions of no unmeasured confounding. Even in randomized studies the mediator may still be correlated with unobserved prognostic variables that affect the outcome, in which case the mediator's role in the causal process may not be inferred without bias. In the behavioural and social science literature very little attention has been given so far to the causal assumptions required for moderated mediation analysis. In this paper we focus on the index for moderated mediation, which measures by how much the mediated effect is larger or smaller for varying levels of the moderator. We show that in linear models this index can be estimated without bias in the presence of unmeasured common causes of the moderator, mediator and outcome under certain conditions. Importantly, one can thus use the test for moderated mediation to support evidence for mediation under less stringent confounding conditions. We illustrate our findings with data from a randomized experiment assessing the impact of being primed with social deception upon observer responses to others’ pain, and from an observational study of individuals who ended a romantic relationship assessing the effect of attachment anxiety during the relationship on mental distress 2 years after the break‐up.     

Identification and Sensitivity Analysis for Average Causal Mediation Effects with Time-Varying Treatments and Mediators: Investigating the Underlying Mechanisms of Kindergarten Retention Policy

Considering that causal mechanisms unfold over time, it is important to investigate the mechanisms over time, taking into account the time-varying features of treatments and mediators. However, identification of the average causal mediation effect in the presence of time-varying treatments and mediators is often complicated by time-varying confounding. This article aims to provide a novel approach to uncovering causal mechanisms in time-varying treatments and mediators in the presence of time-varying confounding. We provide different strategies for identification and sensitivity analysis under homogeneous and heterogeneous effects. Homogeneous effects are those in which each individual experiences the same effect, and heterogeneous effects are those in which the effects vary over individuals. Most importantly, we provide an alternative definition of average causal mediation effects that evaluates a partial mediation effect; the effect that is mediated by paths other than through an intermediate confounding variable. We argue that this alternative definition allows us to better assess at least a part of the mediated effect and provides meaningful and unique interpretations. A case study using ECLS-K data that evaluates kindergarten retention policy is offered to illustrate our proposed approach.     

Is the association between childhood maltreatment and aggressive behavior mediated by hostile attribution bias in women? A discordant twin and sibling study

Understanding the mechanisms behind aggressive behavior (AGG) is vital so that effective prevention and intervention strategies can be developed. Maltreated children are hypothesized to be prone to social information processing biases, such as hostile attribution bias (HAB), which, in turn, may increase the likelihood of behaving aggressively. The first aim of the present study was to replicate findings regarding associations between childhood maltreatment (CM), HAB, and aggression in a population‐based sample of Finnish female twins and their sisters (N = 2,167). However, these associations might not be causal but instead confounded by familial factors, shared between the variables. The second aim was, thus, to test the associations when potential confounding by familial (genetic or common environmental) effects were controlled for using a multilevel discordant twin and sibling design within (a) 379 pairs of twins (n_pairs = 239) or siblings (n_pairs = 140), and (b) within the 131 monozygotic (MZ) twin pairs. Consistent with previous studies, HAB mediated the association between CM and AGG when familial confounding was uncontrolled. No support was found for the mediation when controlling for familial confounding. Between‐pair associations were found between CM and AGG, and between CM and HAB. In addition, within‐pair associations were found between HAB and AGG, and between CM and AGG, however, these were nonsignificant in the discordant MZ analysis, offering the most stringent control of familial confounding. The results indicate the necessity of taking familial confounding into account when investigating the development of AGG.     

A Propensity Score Adjustment for Multiple Group Structural Equation Modeling

In the behavioral and social sciences, quasi-experimental and observational studies are used due to the difficulty achieving a random assignment. However, the estimation of differences between groups in observational studies frequently suffers from bias due to differences in the distributions of covariates. To estimate average treatment effects when the treatment variable is binary, Rosenbaum and Rubin (1983a) proposed adjustment methods for pretreatment variables using the propensity score. However, these studies were interested only in estimating the average causal effect and/or marginal means. In the behavioral and social sciences, a general estimation method is required to estimate parameters in multiple group structural equation modeling where the differences of covariates are adjusted. We show that a Horvitz–Thompson-type estimator, propensity score weighted M estimator (PWME) is consistent, even when we use estimated propensity scores, and the asymptotic variance of the PWME is shown to be less than that with true propensity scores. Furthermore, we show that the asymptotic distribution of the propensity score weighted statistic under a null hypothesis is a weighted sum of independent χ² ₁ variables. We show the method can compare latent variable means with covariates adjusted using propensity scores, which was not feasible by previous methods. We also apply the proposed method for correlated longitudinal binary responses with informative dropout using data from the Longitudinal Study of Aging (LSOA). The results of a simulation study indicate that the proposed estimation method is more robust than the maximum likelihood (ML) estimation method, in that PWME does not require the knowledge of the relationships among dependent variables and covariates.     

权力对基本归因错误的影响

归因对理解和预测环境有着重要作用。归因的两阶段过程理论认为,自动化加工会衍生内归因,进一步的控制化加工才会导致外归因。社会权力会提升自动化加工倾向,因此,社会权力可能会提升基本归因错误的发生。通过四个研究,对此进行验证。研究1-3通过问卷测量,以特质性权力感作为社会权力的指标,其中研究1通过情境判断测验对基本归因错误进行测量,研究2-3采用自陈量表对基本归因错误进行测量,并对社会经济地位进行了控制。研究4则采用启动的方法启动临时性社会权力,探求社会权力与基本归因错误之间的因果关系。四个研究均得出了一致的结论：高社会权力个体更倾向于内归因。     

When does measurement error in covariates impact causal effect estimates? Analytic derivations of different scenarios and an empirical illustration

The average causal treatment effect (ATE) can be estimated from observational data based on covariate adjustment. Even if all confounding covariates are observed, they might not necessarily be reliably measured and may fail to obtain an unbiased ATE estimate. Instead of fallible covariates, the respective latent covariates can be used for covariate adjustment. But is it always necessary to use latent covariates? How well do analysis of covariance (ANCOVA) or propensity score (PS) methods estimate the ATE when latent covariates are used? We first analytically delineate the conditions under which latent instead of fallible covariates are necessary to obtain the ATE. Then we empirically examine the difference between ATE estimates when adjusting for fallible or latent covariates in an applied example. We discuss the issue of fallible covariates within a stochastic theory of causal effects and analyse data of a within-study comparison with recently developed ANCOVA and PS procedures that allow for latent covariates. We show that fallible covariates do not necessarily bias ATE estimates, but point out different scenarios in which adjusting for latent covariates is required. In our empirical application, we demonstrate how latent covariates can be incorporated for ATE estimation in ANCOVA and in PS analysis.     

Inferring causal networks from observations and interventions

Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints.     

Rethinking the Control of Nuisance Variables in Theory Testing

In testing hypotheses, researchers frequently have used multiple regression analysis to control for nuisance variables (i.e., potential confounding variables that are correlated with hypothesized causal variables). In this paper, we highlight limitations of this control strategy, and we discuss fundamental issues that should be considered in deciding whether to use it. Ultimately, we suggest the use of multiple regression analysis sometimes may not improve causal understanding and may actually limit the generalizability of results. Instead of the common practice of controlling for nuisance variables, we suggest that looking at shared variance frequently is a more appropriate test of a theoretical hypothesis.     

Substance abuse and violence: A review of the literature

Most alcohol and drug use occurs among persons who are not violent. However, alcohol and, to a lesser extent, illicit drugs are present in both offenders and victims in many violent events. The links between psychoactive substances and violence involve broad social and economic forces, the settings in which people obtain and consume the substance, and the biological processes that underlie all human behavior. In the case of alcohol, evidence from laboratory and empirical studies support the possibility of a causal role in violent behavior. Similarly, the psychopharmacodynamics of stimulants, such as amphetamines and cocaine, also suggest that these substances could play a contributing role in violent behavior. On the other hand, most real-world studies indicate that this relationship is exceedingly complex and moderated by a host of factors in the individual and the environment. In addition to psychopharmacological effects, substance use may lead to violence through social processes such as drug distribution systems (systemic violence) and violence used to obtain drugs or money for drugs (economic compulsive violence).     

Using full matching to estimate causal effects in nonexperimental studies: examining the relationship between adolescent marijuana use and adult outcomes

Matching methods such as nearest neighbor propensity score matching are increasingly popular techniques for controlling confounding in nonexperimental studies. However, simple k:1 matching methods, which select k well-matched comparison individuals for each treated individual, are sometimes criticized for being overly restrictive and discarding data (the unmatched comparison individuals). The authors illustrate the use of a more flexible method called full matching. Full matching makes use of all individuals in the data by forming a series of matched sets in which each set has either 1 treated individual and multiple comparison individuals or 1 comparison individual and multiple treated individuals. Full matching has been shown to be particularly effective at reducing bias due to observed confounding variables. The authors illustrate this approach using data from the Woodlawn Study, examining the relationship between adolescent marijuana use and adult outcomes.     

Causal graphical views of fixed effects and random effects models

Despite the long-standing discussion on fixed effects (FE) and random effects (RE) models, how and under what conditions both methods can eliminate unmeasured confounding bias has not yet been widely understood in practice. Using a simple pretest–posttest design in a linear setting, this paper translates the conventional algebraic formalization of FE and RE models into causal graphs and provides intuitively accessible graphical explanations about their data-generating and bias-removing processes. The proposed causal graphs highlight that FE and RE models consider different data-generating models. RE models presume a data-generating model that is identical to a randomized controlled trial, while FE models allow for unobserved time-invariant treatment–outcome confounding. Augmenting regular causal graphs that describe data-generating processes by adding the computational structures of FE and RE estimators, the paper visualizes how FE estimators (gain score and deviation score estimators) and RE estimators (quasi-deviation score estimators) offset unmeasured confounding bias. In contrast to standard regression or matching estimators that reduce confounding bias by blocking non-causal paths via conditioning, FE and RE estimators offset confounding bias by deliberately creating new non-causal paths and associations of opposite sign. Though FE and RE estimators are similar in their bias-offsetting mechanisms, the augmented graphs reveal their subtle differences that can result in different biases in observational studies.     

Pro-criminal attitudes,intervention, and recidivism

We review the recent research literature on pro-criminal attitudes (PCAs) as a causal factor of recidivism with a focus on studies on the effectiveness of offender treatment programs targeting PCAs to prevent recidivism. The main conclusions that can be derived from the literature are: (1) the evidence supports the hypothesis that PCAs are related to reoffending; (2) most investigated offender treatment programs tend to reduce PCAs, although the general lack of adequate control group designs does not rule out alternative explanations for this reduction; and (3) there is no conclusive empirical evidence that intervention programs designed to reduce PCAs are effective in reducing recidivism. Empirical research in this area lacks the theoretical and methodological rigor to test causal models of the influence of treatment on reducing PCAs, and effects of PCAs on recidivism. Limitations of the empirical evidence are related to inadequate research designs and/or suboptimal data analysis strategies. Recommendations concerning optimized research designs and data analysis strategies that are likely to provide more conclusive evidence on the relation of PCAs, PCA treatment, and recidivism are given.     

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause‐effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre‐training (or even post‐training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue‐outcome co‐occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.     

Nonlinear Predictive Models for Multiple Mediation Analysis: With an Application to Explore Ethnic Disparities in Anxiety and Depression Among Cancer Survivors

Mediation analysis allows the examination of effects of a third variable (mediator/confounder) in the causal pathway between an exposure and an outcome. The general multiple mediation analysis method (MMA), proposed by Yu et al., improves traditional methods (e.g., estimation of natural and controlled direct effects) to enable consideration of multiple mediators/confounders simultaneously and the use of linear and nonlinear predictive models for estimating mediation/confounding effects. Previous studies find that compared with non-Hispanic cancer survivors, Hispanic survivors are more likely to endure anxiety and depression after cancer diagnoses. In this paper, we applied MMA on MY-Health study to identify mediators/confounders and quantify the indirect effect of each identified mediator/confounder in explaining ethnic disparities in anxiety and depression among cancer survivors who enrolled in the study. We considered a number of socio-demographic variables, tumor characteristics, and treatment factors as potential mediators/confounders and found that most of the ethnic differences in anxiety or depression between Hispanic and non-Hispanic white cancer survivors were explained by younger diagnosis age, lower education level, lower proportions of employment, less likely of being born in the USA, less insurance, and less social support among Hispanic patients.     

The Effects of Children's Exposure to Domestic Violence: A Meta-Analysis and Critique

A wide range of children's developmental outcomes are compromised by exposure to domestic violence, including social, emotional, behavioral, cognitive, and general health functioning. However, there are relatively few empirical studies with adequate control of confounding variables and a sound theoretical basis. We identified 41 studies that provided relevant and adequate data for inclusion in a meta-analysis. Forty of these studies indicated that children's exposure to domestic violence was related to emotional and behavioral problems, translating to a small overall effect (Z _r = .28). Age, sex, and type of outcome were not significant moderators, most likely due to considerable heterogeneity within each of these groups. Co-occurrence of child abuse increased the level of emotional and behavioral problems above and beyond exposure alone, based on 4 available studies. Future research needs are identified, including the need for large-scale longitudinal data and theoretically guided approaches that take into account relevant contextual factors.