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)     

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)     

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)     

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)     

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 self-agency bias in preschoolers' causal inferences.

Preschoolers' causal learning from intentional actions—causal interventions—is subject to a self-agency bias. The authors propose that this bias is evidence-based, in other words, that it is responsive to causal uncertainty. In the current studies, two causes (one child controlled, one experimenter controlled) were associated with one or two effects, first independently, then simultaneously. When initial independent effects were probabilistic, and thus subsequent simultaneous actions were causally ambiguous, children showed a self-agency bias. Children showed no bias when initial effects were deterministic. Further controls established that children's self-agency bias is not a wholesale preference but rather is influenced by uncertainty in causal evidence. These results demonstrate that children's own experience of action influences their causal learning, and the findings suggest possible benefits in uncertain and ambiguous everyday learning contexts. (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)     

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)     

Comparative outcome evaluations of psychotherapies: Guidelines for addressing eight limitations of the gold standard of causal inference.

Although randomized experiments represent the gold standard in causal research, their limitations are easily overlooked, especially in field experiments. Although discussions of the limitations of field experiments typically detail problems of design implementation, field experiments have limitations even if their randomized designs are successfully implemented. This is particularly true of comparative evaluations of psychotherapy programs. Accordingly, this article focuses on eight limitations that pervade such comparative treatment research: (a) Direct and indirect effects are conflated; (b) the investigation of interactions is limited; (c) attention is diverted from background conditions; (d) temporal factors complicate causal inferences; (e) the stable-unit-treatment-value assumption is easily violated; (f) therapist effects can confound therapy method effects; (g) differential effects of treatment methods are small; and (h) generalizability of treatment research findings is indeterminate. In addition to explaining each limitation, the author proposes research strategies for addressing it. The author concludes that attention to these 8 limitations and to the various strategies for overcoming them can increase increase the causal contribution of experimental evaluations of psychotherapies. (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)     

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)     

Temporal predictability facilitates causal learning.

[Correction Notice: An erratum for this article was reported in Vol 140(3) of Journal of Experimental Psychology: General (see record 2011-16270-001). Figure 2 (p. 759) contained an error. The corrected figure appears in the correction.] Temporal predictability refers to the regularity or consistency of the time interval separating events. When encountering repeated instances of causes and effects, we also experience multiple cause–effect temporal intervals. Where this interval is constant it becomes possible to predict when the effect will follow from the cause. In contrast, interval variability entails unpredictability. Three experiments investigated the extent to which temporal predictability contributes to the inductive processes of human causal learning. The authors demonstrated that (a) causal relations with fixed temporal intervals are consistently judged as stronger than those with variable temporal intervals, (b) that causal judgments decline as a function of temporal uncertainty, and (c) that this effect remains undiminished with increased learning time. The results therefore clearly indicate that temporal predictability facilitates causal discovery. The authors considered the implications of their findings for various theoretical perspectives, including associative learning theory, the attribution shift hypothesis, and causal structure models. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

A symbolic-connectionist theory of relational inference and generalization.

The authors present a theory of how relational inference and generalization can be accomplished within a cognitive architecture that is psychologically and neurally realistic. Their proposal is a form of symbolic connectionism: a connectionist system based on distributed representations of concept meanings, using temporal synchrony to bind fillers and roles into relational structures. The authors present a specific instantiation of their theory in the form of a computer simulation model, Learning and Inference with Schemas and Analogies (LISA). By using a kind of self-supervised learning, LISA can make specific inferences and form new relational generalizations and can hence acquire new schemas by induction from examples. The authors demonstrate the sufficiency of the model by using it to simulate a body of empirical phenomena concerning analogical inference and relational generalization. (PsycINFO Database Record (c) 2011 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)     

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)     

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)     

The dispositional inference strikes back: Situational focus and dispositional suppression in causal attribution.

The authors propose that correction of dispositional inferences involves the examination of situational constraints and the suppression of dispositional inferences. They hypothesized that suppression would result in dispositional rebound. In Study 1, participants saw a video of either a free or a forced speaker. Participants shown a forced speaker later made stronger dispositional inferences about a 2nd, free speaker than control participants did. Study 2 provided evidence for higher rebound among participants who reported trying harder to suppress dispositional inferences during the 1st video. In Study 3, participants were asked to focus on situational constraints or to avoid thinking about the speaker's characteristics. Only the latter instructions led to a dispositional rebound. These data support the view that the correction of dispositional inferences involves 2 processes that lead to distinct consequences in subsequent attribution work. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Learning and transfer: A general role for analogical encoding.

Teaching by examples and cases is widely used to promote learning, but it varies widely in its effectiveness. The authors test an adaptation to case-based learning that facilitates abstracting problem-solving schemas from examples and using them to solve further problems: analogical encoding, or learning by drawing a comparison across examples. In 3 studies, the authors examined schema abstraction and transfer among novices learning negotiation strategies. Experiment 1 showed a benefit for analogical learning relative to no case study. Experiment 2 showed a marked advantage for comparing two cases over studying the 2 cases separately. Experiment 3 showed that increasing the degree of comparison support increased the rate of transfer in a face-to-face dynamic negotiation exercise. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

How causal knowledge affects classification: A generative theory of categorization.

Several theories have been proposed regarding how causal relations among features of objects affect how those objects are classified. The assumptions of these theories were tested in 3 experiments that manipulated the causal knowledge associated with novel categories. There were 3 results. The 1st was a multiple cause effect in which a feature's importance increases with its number of causes. The 2nd was a coherence effect in which good category members are those whose features jointly corroborate the category's causal knowledge. These 2 effects can be accounted for by assuming that good category members are those likely to be generated by a category's causal laws. The 3rd result was a primary cause effect, in which primary causes are more important to category membership. This effect can also be explained by a generative account with an additional assumption: that categories often are perceived to have hidden generative causes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)