Blickets and babies: The development of causal reasoning in toddlers and infants.

Previous research has suggested that preschoolers possess a cognitive system that allows them to construct an abstract, coherent representation of causal relations among events. Such a system lets children reason retrospectively when they observe ambiguous data in a rational manner (e.g., D. M. Sobel, J. B. Tenenbaum, & A. Gopnik, 2004). However, there is little evidence that demonstrates whether younger children possess similar inferential abilities. In Experiment 1, the authors extended previous findings with older children to examine 19- and 24-month-olds' causal inferences. Twenty-four-month-olds' inferences were similar to those of preschoolers, but younger children lacked the ability to make retrospective causal inferences, perhaps because of performance limitations. In Experiment 2, the authors designed an eye-tracking paradigm to test younger participants that eliminated various manual search demands. Eight-month-olds' anticipatory eye movements, in response to retrospective data, revealed inferences similar to those of 24-month-olds in Experiment 1 and preschoolers in previous research. These data are discussed in terms of associative reasoning and causal inference. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Expectations and interpretations during causal learning.

In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A–1C, participants' interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Conditional probability versus spatial contiguity in causal learning: Preschoolers use new contingency evidence to overcome prior spatial assumptions.

This study examines preschoolers' causal assumptions about spatial contiguity and how these assumptions interact with new evidence in the form of conditional probabilities. Preschoolers saw a toy that activated in the presence of certain objects. Children were shown evidence for the toy's activation rule in the form of patterns of probability: The toy was more likely to activate either when objects made contact with its surface (on condition) or when objects were several inches above its surface (over condition). In Experiment 1, 61 three-year-olds saw a deterministic activation rule. In Experiments 2 and 3, 48 four-year-olds saw an activation rule that was probabilistic. In Experiment 4, 30 four-year-olds saw a screening-off pattern of activation. In all 4 experiments, children used new evidence in the form of patterns of probability to make accurate causal inferences, even in the face of conflicting prior beliefs about spatial contiguity. However, children were more likely to make correct inferences when causes were spatially contiguous, particularly when faced with ambiguous evidence. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Combining group-based trajectory modeling and propensity score matching for causal inferences in nonexperimental longitudinal data.

A central theme of research on human development and psychopathology is whether a therapeutic intervention or a turning-point event, such as a family break-up, alters the trajectory of the behavior under study. This article describes and applies a method for using observational longitudinal data to make more transparent causal inferences about the impact of such events on developmental trajectories. The method combines 2 distinct lines of research: work on the use of finite mixture modeling to analyze developmental trajectories and work on propensity score matching. The propensity scores are used to balance observed covariates and the trajectory groups are used to control pretreatment measures of response. The trajectory groups also aid in characterizing classes of subjects for which no good matches are available. The approach is demonstrated with an analysis of the impact of gang membership on violent delinquency based on data from a large longitudinal study conducted in Montréal, Canada. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Can being scared cause tummy aches? Naive theories, ambiguous evidence, and preschoolers' causal inferences.

Causal learning requires integrating constraints provided by domain-specific theories with domain-general statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate causes co-occurred with an effect. Evidence was presented in the form: AB→E; CA→E; AD→E; and so forth. In 1 story, all variables came from the same domain; in the other, the recurring candidate cause, A, came from a different domain (A was a psychological cause of a biological effect). After receiving this statistical evidence, children were asked to identify the cause of the effect on a new trial. Consistent with the predictions of a Bayesian model, all children were more likely to identify A as the cause within domains than across domains. Whereas 3.5-year-olds learned only from the within-domain evidence, 4- and 5-year-olds learned from the cross-domain evidence and were able to transfer their new expectations about psychosomatic causality to a novel task. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Judgment dissociation theory: An analysis of differences in causal, counterfactual and covariational reasoning.

Research suggests that causal judgment is influenced primarily by counterfactual or covariational reasoning. In contrast, the author of this article develops judgment dissociation theory (JDT), which predicts that these types of reasoning differ in function and can lead to divergent judgments. The actuality principle proposes that causal selections focus on antecedents that are sufficient to generate the actual outcome. The substitution principle proposes that ad hoc categorization plays a key role in counterfactual and covariational reasoning such that counterfactual selections focus on antecedents that would have been sufficient to prevent the outcome or something like it and covariational selections focus on antecedents that yield the largest increase in the probability of the outcome or something like it. The findings of 4 experiments support JDT but not the competing counterfactual and covariational accounts. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The special status of actions in causal reasoning in rats.

A. P. Blaisdell, K. Sawa, K. J. Leising, and M. R. Waldmann (2006) reported evidence for causal reasoning in rats. After learning through Pavlovian observation that Event A (a light) was a common cause of Events X (an auditory stimulus) and F (food), rats predicted F in the test phase when they observed Event X as a cue but not when they generated X by a lever press. Whereas associative accounts predict associations between X and F regardless of whether X is observed or generated by an action, causal-model theory predicts that the intervention at test should lead to discounting of A, the regular cause of X. The authors report further tests of causal-model theory. One key prediction is that full discounting should be observed only when the alternative cause is viewed as deterministic and independent of other events, 2 hallmark features of actions but not necessarily of arbitrary events. Consequently, the authors observed discounting with only interventions but not other observable events (Experiments 1 and 2). Moreover, rats were capable of flexibly switching between observational and interventional predictions (Experiment 3). Finally, discounting occurred on the very first test trial (Meta-Analysis). These results confirm causal-model theory but refute associative accounts. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Knowing who dunnit: Infants identify the causal agent in an unseen causal interaction.

Preverbal infants can represent the causal structure of events, including distinguishing the agentive and receptive roles and categorizing entities according to stable causal dispositions. This study investigated how infants combine these 2 kinds of causal inference. In Experiments 1 and 2, 9.5-month-olds used the position of a human hand or a novel puppet (causal agents), but not a toy train (an inert object), to predict the subsequent motion of a beanbag. Conversely, in Experiment 3, 10- and 7-month-olds used the motion of the beanbag to infer the position of a hand but not of a toy block. These data suggest that preverbal infants expect a causal agent as the source of motion of an inert object. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A general approach to causal mediation analysis.

Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the inability to specify the key identification assumption, and the difficulty of extending the framework to nonlinear models. In this article, we propose an alternative approach that overcomes these limitations. Our approach is general because it offers the definition, identification, estimation, and sensitivity analysis of causal mediation effects without reference to any specific statistical model. Further, our approach explicitly links these 4 elements closely together within a single framework. As a result, the proposed framework can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and various types of outcome variables. The general definition and identification result also allow us to develop sensitivity analysis in the context of commonly used models, which enables applied researchers to formally assess the robustness of their empirical conclusions to violations of the key assumption. We illustrate our approach by applying it to the Job Search Intervention Study. We also offer easy-to-use software that implements all our proposed methods. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Word, thought, and deed: The role of object categories in children's inductive inferences and exploratory play.

Previous research (e.g., S. A. Gelman & E. M. Markman, 1986; A. Gopnik & D. M. Sobel, 2000) suggests that children can use category labels to make inductive inferences about nonobvious causal properties of objects. However, such inductive generalizations can fail to predict objects' causal properties when (a) the property being projected varies within the category, (b) the category is arbitrary (e.g., things smaller than a bread box), or (c) the property being projected is due to an exogenous intervention rather than intrinsic to the object kind. In 4 studies, the authors showed that preschoolers (M = 48 months; range = 42-57 months) were sensitive to these constraints on induction and selectively engaged in exploration when evidence about objects' causal properties conflicted with inductive generalizations from the objects' kind to their causal powers. This suggests that the exploratory actions children generate in free play could support causal learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Analogical and category-based inference: A theoretical integration with Bayesian causal models.

A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source information at various levels of abstraction (including both specific instances and more general categories), coupled with prior causal knowledge, to build a causal model for a target situation, which in turn constrains inferences about the target. We propose a computational theory in the framework of Bayesian inference and test its predictions (parameter-free for the cases we consider) in a series of experiments in which people were asked to assess the probabilities of various causal predictions and attributions about a target on the basis of source knowledge about generative and preventive causes. The theory proved successful in accounting for systematic patterns of judgments about interrelated types of causal inferences, including evidence that analogical inferences are partially dissociable from overall mapping quality. (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)     

Campbell and Rubin: A primer and comparison of their approaches to causal inference in field settings.

This article compares Donald Campbell’s and Donald Rubin’s work on causal inference in field settings on issues of epistemology, theories of cause and effect, methodology, statistics, generalization, and terminology. The two approaches are quite different but compatible, differing mostly in matters of bandwidth versus fidelity. Campbell’s work demonstrates broad narrative scope that covers a wide array of concepts related to causation, with a powerful appreciation for human fallibility in making causal judgments, with a more elaborate theory of cause and generalization, and with a preference for design over analysis. Rubin’s approach is a more narrow and formal quantitative analysis of effect estimation, sharing a preference for design but best known for analysis, with compelling quantitative approaches to obtaining unbiased quantitative effect estimates from nonrandomized designs and with comparatively little to say about generalization. Much could be gained by joining the emphasis on design in Campbell with the emphasis on analysis in Rubin. However, the 2 approaches also speak modestly different languages that leave some questions about their total commensurability that only continued dialogue can fully clarify. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Spontaneous assimilation of continuous values and temporal information in causal induction.

Existing models of causal induction primarily rely on the contingency between the presence and the absence of a causal candidate and an effect. Yet, classification of observations into these four types of covariation data may not be straightforward because (a) most causal candidates, in real life, are continuous with ambiguous, intermediate values and because (b) effects may unfold after some temporal lag, providing ambiguous contingency information. Although past studies suggested various reasons why ambiguous information may not be used during causal induction, the authors examined whether learners spontaneously use ambiguous information through a process called causal assimilation. In particular, the authors examined whether learners willingly place ambiguous observations into one of the categories relevant to the causal hypothesis, in accordance with their current causal beliefs. In Experiment 1, people's frequency estimates of contingency data reflected that information ambiguous along a continuous quantity dimension was spontaneously categorized and assimilated in a causal induction task. This assimilation process was moderated by the strength of the upheld causal hypothesis (Experiment 2), could alter the overall perception of a causal relationship (Experiment 3), and could occur over temporal sequences (Experiment 4). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Inference-based retrospective revaluation in human causal judgments requires knowledge of within-compound relationships.

In an allergist causal-judgment task, food compounds were followed by an allergic reaction (e.g., AB+), and then 1 cue (A) was revalued. Experiment 1, in which participants who were instructed that whatever was true about one element of a causal compound was also true of the other, showed a reverse of the standard retrospective revaluation effect. That is, ratings of B were higher when A was causal (A+) than when A was safe (A-). This effect was taken to reflect inferential reasoning, not an associative mechanism. In Experiment 2, within-compound associations were found to be necessary to produce this inference-based revaluation. Therefore, evidence that within-compound associations are necessary for retrospective revaluation is consistent with the inferential account of causal judgments. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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)     

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)