Structure and strength in causal induction

We present a framework for the rational analysis of elemental causal induction-learning about the existence of a relationship between a single cause and effect-based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relationship might be. We show that two leading rational models of elemental causal induction, DeltaP and causal power, both estimate causal strength, and we introduce a new rational model, causal support, that assesses causal structure. Causal support predicts several key phenomena of causal induction that cannot be accounted for by other rational models, which we explore through a series of experiments. These phenomena include the complex interaction between DeltaP and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal learning from rates. Causal support also provides a better account of a number of existing datasets than either DeltaP or causal power.     

Learning to learn causal models

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning.     

Summarizing information by means of causal sentences through causal graphs

The objective of this work is to propose a complete system able to extract causal sentences from a set of text documents, select the causal sentences contained, create a causal graph in base to a given concept using as source these causal sentences, and finally produce a text summary gathering all the information connected by means of this causal graph. This procedure has three main steps. The first one is focused in the extraction, filtering and selection of those causal sentences that could have relevant information for the system. The second one is focused on the composition of a suitable causal graph, removing redundant information and solving ambiguity problems. The third step is a procedure able to read the causal graph to compose a suitable answer to a proposed causal question by summarizing the information contained in it.     

The power PC theory and causal powers: comment on Cheng (1997) and Novick and Cheng (2004)

It has been claimed that the power PC theory reconciles regularity and power theories of causal judgment by showing how contingency information is used for inferences about unobservable causal powers. Under the causal powers theory causal relations are understood as generative relations in which a causal power of one thing acts on a liability of another thing under some releasing condition. These 3 causal roles are implicit or explicit in all causal interpretations. The power PC theory therefore fails to reconcile power theories and regularity theories because it has a fundamentally different definition of power and does not accommodate the tripartite causal role distinction. Implications of this distinction are drawn out.     

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.     

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.     

Great oaks from giant acorns grow: How causal‐impact judgments depend on the strength of a cause

Causal impact is maximal when weak causes have strong effects. Do people understand this logic when they assess causal impact? In four experiments, participants judged the causal impact of strong or weak dietary treatments leading to strong or weak health effects in fictitious health studies. Rather than following the ratio of effect strength to treatment strength, judgments were influenced by three aspects of the detectability of a cause–effect relation. First, because detectability depends on the effect being strong more than on the cause being subtle, causal judgments were mainly determined by effect strength, whereas the strength of the causal treatment necessary to induce an effect was often neglected. Second, if causal input was not ignored, judgments increased when the maximal covariation between a strong causal treatment and a strong effect rendered the causal link most detectable. Or, third, causal judgments increased when a plausible causal schema facilitated detection. Consistent with sampling models of judgment and decision making, causal‐impact ratings were driven by an uncritical assessment of a detectable difference in a study sample. However, ratings were insensitive to the logical implications of the underlying causal treatment that was necessary to induce a detectable effect. Copyright © 2010 John Wiley & Sons, Ltd.     

Memory-Based Hypothesis Formation: Heuristic Learning of Commonsense Causal Relations from Text

We present a memory-based approach to learning commonsense causal relations from episodic text. The method relies on dynamic memory that consists of events, event schemata, episodes, causal heuristics, and causal hypotheses. The learning algorithms are based on applying causal heuristics to precedents of new information. The heuristics are derived from principles of causation, and, to a limited extent, from domain-related causal reasoning. Learning is defined as finding—and later augmenting—inter-episodal and intea-episodal causal connections. The learning algorithms enable inductive generalization of causal associations into AND/OR graphs. The methodology has been implemented and tested in the program NEXUS.     

Compound cue processing within the fast and frugal heuristics approach in nonlinearly separable environments

Three experiments investigated whether participants used Take The Best (TTB) Configural, a fast and frugal heuristic that processes configurations of cues when making inferences concerning which of two alternatives has a higher criterion value. Participants were presented with a compound cue that was nonlinearly separable from its elements. The compound was highly valid in Experiments 1 and 2, but invalid in Experiment 3. Participants’ causal mental models were manipulated via instructions: participants were either told that cues acted through the same causal mechanism (configural causal model), through different causal mechanisms (elemental causal model), or the causal mechanisms were not specified (neutral causal model). A high percentage of participants used TTB-Configural when they had a configural causal model and a highly valid compound existed, suggesting that causal knowledge can be incorporated in otherwise very basic cognitive mechanisms to allow fine-grained adaptation to complex task structures.     

Toward a causal realist account of causal understanding

Within a limited domain, humans can perceive causal relations directly. The term causal realism is used to denote this psychological hypothesis. The domain of causal realism is in actions upon objects and haptic perception of the effects of those actions: When we act upon an object we cannot be mistaken about the fact that we are acting upon it and perceive the causal relation directly through mechanoreceptors. Experiences of actions upon objects give rise to causal knowledge that can be used in the interpretation of perceptual input. Phenomenal causality, the occurrence of causal impressions in visual perception, is a product of the application of acquired causal knowledge in the automatic perceptual interpretation of appropriate stimuli. Causal realism could constitute the foundation on which all causal perception, judgment, inference, attribution, and knowledge develop.     

Causal Relations and Explanatory Strategies in Physics

Many philosophers now regard causal approaches to explanation as highly promising, even in physics. This is due in large part to James Woodward's influential argument that a wide variety of scientific explanations are causal, based on his interventionist approach to causation. This article argues that some derivations describing causal relations and satisfying Woodward's criteria for causal explanation fail to be explanatory. Further, causal relations are unnecessary for a range of explanations, widespread in physics, involving highly idealized models. These constitute significant limitations on the scope of causal explanation. We have good reason to doubt that causal explanation is as widespread or important in physics as Woodward and other proponents maintain.     

Knowledge mediates the timeframe of covariation assessment in human causal induction

How do humans discover causal relations when the effect is not immediately observable? Previous experiments have uniformly demonstrated detrimental effects of outcome delays on causal induction. These findings seem to conflict with everyday causal cognition, where humans can apparently identify long-term causal relations with relative ease. Three experiments investigated whether the influence of delay on adult human causal judgements is mediated by experimentally induced assumptions about the timeframe of the causal relation in question, as suggested by Einhorn and Hogarth (1986). Causal judgements generally decreased when a delay separated cause and effect. This decrease was less pronounced when the thematic context of the causal relation induced participants to expect a delay. Experiment 3 ruled out an alternative explanation of the effect based on variations of cue and outcome saliencies, and showed that detrimental effects of delay are reduced even more when instructions explicitly mentioned the timeframe of the causal relation in question. Knowledge thus mediates the impact of delay on human causal judgement. Implications for contemporary theories of human causal induction are discussed.     

Systems without a graphical causal representation

There are simple mechanical systems that elude causal representation. We describe one that cannot be represented in a single directed acyclic graph. Our case suggests limitations on the use of causal graphs for causal inference and makes salient the point that causal relations among variables depend upon details of causal setups, including values of variables.     

Causal Exclusion and Causal Bayes Nets

In this paper I reconstruct and evaluate the validity of two versions of causal exclusion arguments within the theory of causal Bayes nets. I argue that supervenience relations formally behave like causal relations. If this is correct, then it turns out that both versions of the exclusion argument are valid when assuming the causal Markov condition and the causal minimality condition. I also investigate some consequences for the recent discussion of causal exclusion arguments in the light of an interventionist theory of causation such as Woodward's ( 2003 ) and discuss a possible objection to my causal Bayes net reconstruction.     

A causal model for causal priority

Recent attempts to fix the direction of causal priority without reference to the direction of temporal priority have begun with an analysis of the causal relation itself. I offer a method, based on causal modelling theory, designed to determine the direction of causal priority while remaining as agnostic as possible about the nature of the causal relation.     

Self-Efficacy and Causal Attributions: Direct and Reciprocal Links

This study examines Bandura's (1986, 1997a) propositions that self-efficacy provides information from which causal attributions are made and that causal attributions, in turn, influence formation of subsequent self-efficacy expectations. We developed a conceptual rationale for and empirically tested 2 sets of hypotheses pertaining to direct and reciprocal links between self-efficacy and causal attributions. Effects of causal attributions and subsequently formed self-efficacy on subsequent task performance were also investigated. Results support the existence of direct and reciprocal links between self-efficacy and causal attributions. We found interactive effects between self-efficacy and performance feedback on causal attributions, and a mediating effect of causal attributions on the formation of subsequent self-efficacy beliefs. Causal attributions and subsequent self-efficacy also significantly predicted subsequent performance.     

Does causal action facilitate causal perception in infants younger than 6 months of age?

Previous research has established that infants are unable to perceive causality until 6¼ months of age. The current experiments examined whether infants’ ability to engage in causal action could facilitate causal perception prior to this age. In Experiment 1, 4½‐month‐olds were randomly assigned to engage in causal action experience via Velcro sticky mittens or not engage in causal action because they wore non‐sticky mittens. Both groups were then tested in the visual habituation paradigm to assess their causal perception. Infants who engaged in causal action – but not those without this causal action experience – perceived the habituation events as causal. Experiment 2 used a similar design to establish that 4½‐month‐olds are unable to generalize their own causal action to causality observed in dissimilar objects. These data are the first to demonstrate that infants under 6 months of age can perceive causality, and have implications for the mechanisms underlying the development of causal perception.