An economist’s perspective on Shadish (2010) and West and Thoemmes (2010).

In Shadish (2010) and West and Thoemmes (2010), the authors contrasted 2 approaches to causality. The first originated in the psychology literature and is associated with work by Campbell (e.g., Shadish, Cook, & Campbell, 2002), and the second has its roots in the statistics literature and is associated with work by Rubin (e.g., Rubin, 2006). In this article, I discuss some of the issues raised by Shadish and by West and Thoemmes. I focus mostly on the impact the 2 approaches have had on research in a 3rd field, economics. In economics, the ideas of both Campbell and Rubin have been very influential, with some of the methods they developed now routinely taught in graduate programs and routinely used in empirical work and other methods receiving much less attention. At the same time, economists have added to the understanding of these methods and through these extensions have further improved researchers’ ability to draw causal inferences in observational studies. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Correction to Greville and Buehner (2010).

Reports an error in "Temporal predictability facilitates causal learning" by W. James Greville and Marc J. Buehner (Journal of Experimental Psychology: General, 2010[Nov], Vol 139[4], 756-771). Figure 2 (p. 759) contained an error. The corrected figure appears in the correction. (The following abstract of the original article appeared in record 2010-22538-005.) 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)     

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)     

Concerns regarding the traditional paradigm for causal research: The unified paradigm and causal research in scientific psychology.

The traditional paradigm refers to the assumption held by most methodologists and researchers that causal research must be defined in terms of the causal powers evident in a closed system. The traditional paradigm does not concord, however, with the nature of scientific theories often cited in the methodological and research literature. The unified paradigm is introduced and causal research defined in terms of the causal powers evident in an open system. Notable implications of the unified paradigm are that experimental methods do not provide a better opportunity than modeling methods to conduct a causal analysis and that the nomenclature often used to describe the validity of causal conclusions must be amended. Additional implications of the unified paradigm are discussed and includes a comparison of the traditional paradigm and the unified paradigm when applied to treatment-outcome research. (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)     

Learning from others' mistakes? Limits on understanding a trap-tube task by young chimpanzees (Pan troglodytes) and children (Homo sapiens).

A trap-tube task was used to determine whether chimpanzees (Pan troglodytes) and children (Homo sapiens) who observed a model's errors and successes could master the task in fewer trials than those who saw only successes. Two- to 7-year-old chimpanzees and 3- to 4-year-old children did not benefit from observing errors and found the task difficult. Two of the 6 chimpanzees developed a successful anticipatory strategy but showed no evidence of representing the core causal relations involved in trapping. Three- to 4-year-old children showed a similar limitation and tended to copy the actions of the demonstrator, irrespective of their causal relevance. Five- to 6-year-old children were able to master the task but did not appear to be influenced by social learning or benefit from observing errors. (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)     

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)     

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)     

Na?ve theories of causal force and compression of elapsed time judgments.

Recent research has shown that when people perceive a causal relation between 2 events, they “compress” the intervening elapsed time. The present work shows that a na?ve mechanical–physical conception of causality, in which causal forces are believed to dissipate over time, underlies the estimates of shorter elapsed time. Being primed with alternative, nondissipative causal mechanisms and having the cognitive capacity to consider such mechanisms moderates the compression effect. The studies rule out similarity, mnemonic association, and anchoring as alternative accounts for the effect. Taken together, the findings support the hypothesis that causal cognition plays a major role in judgments of elapsed time. The implications of the compression effect on the timing of future actions, persistence, and causal learning are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal relations and feature similarity in children's inductive reasoning.

Four experiments examined the development of property induction on the basis of causal relations. In the first 2 studies, 5-year-olds, 8-year-olds, and adults were presented with triads in which a target instance was equally similar to 2 inductive bases but shared a causal antecedent feature with 1 of them. All 3 age groups used causal relations as a basis for property induction, although the proportion of causal inferences increased with age. Subsequent experiments pitted causal relations against featural similarity in induction. It was found that adults and 8-year-olds, but not 5-year-olds, preferred shared causal relations over strong featural similarity as a basis for induction. The implications for models of inductive reasoning and development are discussed. (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)     

Representing causation.

The dynamics model, which is based on L. Talmy's (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3-D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a physics simulator. Experiments 1-3 showed that causal judgments are based on several forces, not just one. Experiment 4 demonstrated that people compute the resultant of forces using a qualitative decision rule. Experiments 5 and 6 showed that a dynamics approach extends to the representation of social causation. Implications for the relationship between causation and time are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal learning mechanisms in very young children: Two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation.

Three studies investigated whether young children make accurate causal inferences on the basis of patterns of variation and covariation. Children were presented with a new causal relation by means of a machine called the "blicket detector." Some objects, but not others, made the machine light up and play music. In the first 2 experiments, children were told that "blickets make the machine go" and were then asked to identify which objects were "blickets." Two-, 3-, and 4-year-old children were shown various patterns of variation and covariation between two different objects and the activation of the machine. All 3 age groups took this information into account in their causal judgments about which objects were blickets. In a 3rd experiment, 3- and 4-year-old children used the information when they were asked to make the machine stop. These results are related to Bayes-net causal graphical models of causal learning. (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)     

Causal beliefs and conditioned responses: Retrospective revaluation induced by experience and by instruction.

The author tested causal beliefs and conditioned responses in a task involving retrospective revaluation of the causal status of a target cue with respect to electric shock. Successful revaluation was observed on both self-report shock expectancy and skin conductance, whether the training trials were directly experienced, described, or partly experienced and partly described. The results contradict models that link anticipatory conditioned responses to a separate or earlier process from that underlying explicit causal knowledge. They suggest instead that a single learning process gives rise to propositional knowledge that (a) drives anticipatory responding, (b) forms the basis for self-reported causal beliefs, and (c) can be combined with other knowledge, provided either by experience or symbolically, to generate inferences such as retrospective revaluation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The priority of just-identified, recursive models.

Theories often place constraints on causal relationships, and such constraints are often assessed with causal models. Causal models should be recursive and just identified because cause is recursive and is more likely to be just identified than overidentified. A just-identified, recursive model (JIRM) is specified that satisfies both requirements and that can be used to assess a wide range of causal implications in either a norm-referenced or criterion-referenced manner. P. E. Meehl and N. G. Waller (2002) proposed an innovative method for theory appraisal called the delete one-add one (D1 -A1) method, which assesses a relatively narrow range of causal implications, allows nonrecursive models, and is only norm referenced. The JIRM and D1-A1 methods are compared. (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 Causal-Model Theory of Conceptual Representation and Categorization.

This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches. (PsycINFO Database Record (c) 2010 APA, all rights reserved)