A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal inference and developmental psychology.

Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether the risk factor actually causes outcomes. Random assignment is not possible in many instances, and for that reason, psychologists must rely on observational studies. Such studies identify associations, and causal interpretation of such associations requires additional assumptions. Research in developmental psychology generally has relied on various forms of linear regression, but this methodology has limitations for causal inference. Fortunately, methodological developments in various fields are providing new tools for causal inference—tools that rely on more plausible assumptions. This article describes the limitations of regression for causal inference and describes how new tools might offer better causal inference. This discussion highlights the importance of properly identifying covariates to include (and exclude) from the analysis. This discussion considers the directed acyclic graph for use in accomplishing this task. With the proper covariates having been chosen, many of the available methods rely on the assumption of “ignorability.” The article discusses the meaning of ignorability and considers alternatives to this assumption, such as instrumental variables estimation. Finally, the article considers the use of the tools discussed in the context of a specific research question, the effect of family structure on child development. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Property transmission: An explanatory account of the role of similarity information in causal inference.

Many kinds of common and easily observed causal relations exhibit property transmission, which is a tendency for the causal object to impose its own properties on the effect object. It is proposed that property transmission becomes a general and readily available hypothesis used to make interpretations and judgments about causal questions under conditions of uncertainty, in which property transmission functions as a heuristic. The property transmission hypothesis explains why and when similarity information is used in causal inference. It can account for magical contagion beliefs, some cases of illusory correlation, the correspondence bias, overestimation of cross-situational consistency in behavior, nonregressive tendencies in prediction, the belief that acts of will are causes of behavior, and a range of other phenomena. People learn that property transmission is often moderated by other factors, but under conditions of uncertainty in which the operation of relevant other factors is unknown, it tends to exhibit a pervasive influence on thinking about causality. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Collider Principle in Causal Reasoning: Why the Monty Hall Dilemma Is So Hard.

The authors tested the thesis that people find the Monty Hall dilemma (MHD) hard because they fail to understand the implications of its causal structure, a collider structure in which 2 independent causal factors influence a single outcome. In 4 experiments, participants performed better in versions of the MHD involving competition, which emphasizes causality. This manipulation resulted in more correct responses to questions about the process in the MHD and a counterfactual that changed its causal structure. Correct responses to these questions were associated with solving the MHD regardless of condition. In addition, training on the collider principle transferred to a standard version of the MHD. The MHD taps a deeper question: When is knowing about one thing informative about another? (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Introduction to the special section on Campbell’s and Rubin’s conceptualizations of causality.

Causality plays a fundamental role in scientific explanation. This introduction describes 2 target articles and 3 commentaries on 2 influential perspectives on causal inference, one developed by Donald Campbell and the other developed by Donald Rubin. One goal of this special section is to introduce Rubin’s causal model to psychologists who may be largely unfamiliar with it. Another goal is to compare Rubin’s conceptualization with Campbell’s perspective, to enrich readers’ understanding of both views. All of the authors of this special section perceive many similarities between the 2 approaches. Even so, by comparing and contrasting the 2 perspectives, the authors also believe that it is possible to strengthen both approaches. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The role of causal models in analogical inference.

Computational models of analogy have assumed that the strength of an inductive inference about the target is based directly on similarity of the analogs and in particular on shared higher order relations. In contrast, work in philosophy of science suggests that analogical inference is also guided by causal models of the source and target. In 3 experiments, the authors explored the possibility that people may use causal models to assess the strength of analogical inferences. Experiments 1-2 showed that reducing analogical overlap by eliminating a shared causal relation (a preventive cause present in the source) from the target increased inductive strength even though it decreased similarity of the analogs. These findings were extended in Experiment 3 to cross-domain analogical inferences based on correspondences between higher order causal relations. Analogical inference appears to be mediated by building and then running a causal model. The implications of the present findings for theories of both analogy and causal inference are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal client models in selecting effective interventions: A cognitive mapping study.

An important reason to choose an intervention to treat psychological problems of clients is the expectation that the intervention will be effective in alleviating the problems. The authors investigated whether clinicians base their ratings of the effectiveness of interventions on models that they construct representing the factors causing and maintaining a client's problems. Forty clinical child psychologists drew causal models and rank ordered interventions according to their expected effectiveness for 2 cases. The authors found that different clinicians constructed different causal models for the same client. Also, the authors found low to moderate agreement about the effectiveness of different interventions. Nevertheless, the authors could predict clinicians' ratings of effectiveness from their individual causal models. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Mechanisms of predictive and diagnostic causal induction.

In predictive causal inference, people reason from causes to effects, whereas in diagnostic inference, they reason from effects to causes. Independently of the causal structure of the events, the temporal structure of the information provided to a reasoner may vary (e.g., multiple events followed by a single event vs. a single event followed by multiple events). The authors report 5 experiments in which causal structure and temporal information were varied independently. Inferences were influenced by temporal structure but not by causal structure. The results are relevant to the evaluation of 2 current accounts of causal induction, the Rescorla-Wagner (R. A. Rescorla & A. R. Wagner, 1972) and causal model theories (M. R. Waldmann & K. J. Holyoak, 1992). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal attribution of emotion in kindergarten children.

A measure of causal attribution of emotion using a simple, realistic task with responses requested in segments was employed to investigate kindergartners' understanding of the causes of emotions in others. Results challenge previous research suggesting that preoperational children cannot demonstrate an understanding of causality. Significant, positive correlations between causal attribution scores and teacher ratings of role-taking ability for girls suggested a relationship between causal attribution of emotion and social role-taking skills. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal status and coherence in causal-based categorization.

Research has documented two effects of interfeature causal knowledge on classification. A causal status effect occurs when features that are causes are more important to category membership than their effects. A coherence effect occurs when combinations of features that are consistent with causal laws provide additional evidence of category membership. In this study, we found that stronger causal relations led to a weaker causal status effect and a stronger coherence effect (Experiment 1), that weaker alternative causes led to stronger causal status and coherence effects (Experiment 2), and that “essentialized” categories led to a stronger causal status effect (Experiment 3), albeit only for probabilistic causal links (Experiment 4). In addition, the causal status effect was mediated by features' subjective category validity, the probability they occur in category members. These findings were consistent with a generative model of categorization but inconsistent with an alternative model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Average causal effects from nonrandomized studies: A practical guide and simulated example.

In a well-designed experiment, random assignment of participants to treatments makes causal inference straightforward. However, if participants are not randomized (as in observational study, quasi-experiment, or nonequivalent control-group designs), group comparisons may be biased by confounders that influence both the outcome and the alleged cause. Traditional analysis of covariance, which includes confounders as predictors in a regression model, often fails to eliminate this bias. In this article, the authors review Rubin's definition of an average causal effect (ACE) as the average difference between potential outcomes under different treatments. The authors distinguish an ACE and a regression coefficient. The authors review 9 strategies for estimating ACEs on the basis of regression, propensity scores, and doubly robust methods, providing formulas for standard errors not given elsewhere. To illustrate the methods, the authors simulate an observational study to assess the effects of dieting on emotional distress. Drawing repeated samples from a simulated population of adolescent girls, the authors assess each method in terms of bias, efficiency, and interval coverage. Throughout the article, the authors offer insights and practical guidance for researchers who attempt causal inference with observational data. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The extent of individual differences in the perception of causality.

Films were made of sequences of movement similar to those used by Michotte in his experiments on causality. These were presented to 181 subjects who had to describe in writing what they saw in each film. In addition, they were asked to complete the Cottschaldt Figures Test and an intelligence test. It was found that the number of causal responses was considerably fewer than that predicted by Michotte. In addition, the number of causal responses to films of the entraining effect was equal to that made to films in which ampliation of the movement did not occur. A relationship was also observed between level of intelligence and type of response made to the films. An attempt is made at giving an explanation for the discrepancy between these findings and those reported by Michotte. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal attributions in posttraumatic stress disorder: Implications for clinical research and practice.

In this paper we seek to illuminate connections among basic research findings in cognition and causal inference, clinical research on the treatment of Posttraumatic Stress Disorder (PTSD), and the practices of clinicians who work with trauma survivors. We examine one particular (and, we believe, important) aspect of PTSD: The creation and maintenance of causal attributions about trauma. We suggest that elements of two principal theories of causal induction (the connectionist model and the "Power PC" causal power model) clarify the role of causal attributions in creating and sustaining the symptoms of PTSD. By exploring the role of causal attributions in creating and sustaining posttraumatic symptoms, we hope to understand better the subjective experience of trauma and its sequelae. We then suggest new directions for clinical research on cognitive restructuring in PTSD patients as well as ideas for optimizing attribution-based therapies for trauma survivors. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal inference and related statistical methods

Statistical approaches for evaluating causal effects and for discovering causal networks are discussed in this paper.A causal relation between two variables is different from an association or correlation between them.An association measurement between two variables and may be changed dramatically from positive to negative by omitting a third variable,which is called Yule-Simpson paradox.We shall discuss how to evaluate the causal effect of a treatment or exposure on an outcome to avoid the phenomena of Yule-Simpson paradox. Surrogates and intermediate variables are often used to reduce measurement costs or duration when measurement of endpoint variables is expensive,inconvenient,infeasible or unobservable in practice.There have been many criteria for surrogates.However,it is possible that for a surrogate satisfying these criteria,a treatment has a positive effect on the surrogate,which in turn has a positive effect on the outcome,but the treatment has a negative effect on the outcome,which is called the surrogate paradox.We shall discuss criteria for surrogates to avoid the phenomena of the surrogate paradox. Causal networks which describe the causal relationships among a large number of variables have been applied to many research fields.It is important to discover structures of causal networks from observed data.We propose a recursive approach for discovering a causal network in which a structural learning of a large network is decomposed recursively into learning of small networks.Further to discover causal relationships,we present an active learning approach in terms of external interventions on some variables.When we focus on the causes of an interest outcome, instead of discovering a whole network,we propose a local learning approach to discover these causes that affect the outcome.     

Yes, but what’s the mechanism? (don’t expect an easy answer).

Psychologists increasingly recommend experimental analysis of mediation. This is a step in the right direction because mediation analyses based on nonexperimental data are likely to be biased and because experiments, in principle, provide a sound basis for causal inference. But even experiments cannot overcome certain threats to inference that arise chiefly or exclusively in the context of mediation analysis—threats that have received little attention in psychology. The authors describe 3 of these threats and suggest ways to improve the exposition and design of mediation tests. Their conclusion is that inference about mediators is far more difficult than previous research suggests and is best tackled by an experimental research program that is specifically designed to address the challenges of mediation analysis. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Assessing Interactive Causal Influence.

The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory. Unlike earlier models, the new theory derives (a) the conditions under which covariation implies conjunctive causation and (b) functions relating observable events to unobservable conjunctive causal strength. This psychological theory, which concerns simple cases involving 2 binary candidate causes and a binary effect, raises questions about normative statistics for testing causal hypotheses regarding categorical data resulting from discrete variables. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Seeing Versus Doing: Two Modes of Accessing Causal Knowledge.

The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, whose parameters were learned in a purely observational learning phase, depending on whether learners believe that an event within the model has been merely observed ("seeing") or was actively manipulated ("doing"). The predictions reflect sensitivity both to the structure of the causal models and to the size of their parameters. This competency is remarkable because the predictions for potential interventions were very different from the patterns that had actually been observed. Whereas associative and probabilistic theories fail, recent developments of causal Bayes net theories provide tools for modeling this competency. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Blocking and Unblocking in Human Causal Learning.

Three experiments sought to develop the suggestion that, under some circumstances, common associative learning mechanisms might underlie animal conditioning and human causal learning, by demonstrating, in humans, an effect analogous to the unblocking by reinforcer omission observed in animal conditioning. Experiment 1 found no such effect. Experiment 2, designed to prevent inhibitory influences that might have masked excitatory unblocking in Experiment 1, demonstrated unblocking, indicating common human-animal associative learning mechanisms in which the associability of a stimulus varies as a function of its predictive history. Experiment 3, using a similar design but with a procedure promoting application of rational inference processes, failed to detect the same unblocking effect, indicating that associative and cognitive mechanisms may influence human causal learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)