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, ∆P and causal power, both estimate causal strength, and 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 ∆P 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 ∆P or causal power.

The rationality of human causal judgments has been the focus of a great deal of recent research. We argue against two major trends in this research, and for a quite different way of thinking about causal mechanisms and probabilistic data. Our position rejects a false dichotomy between "mechanistic " and "probabilistic " analyses of causal inference-- a dichotomy that both overlooks the nature of the evidence that supports the induction of mechanisms and misses some important probabilistic implications of mechanisms. This dichotomy has obscured an alternative conception of causal learning: for discrete events, a central adaptive task is to induce causal mechanisms in the environment from probabilistic data and prior knowledge. Viewed from this perspective, it is apparent that the probabilistic norms assumed in the human causal judgment literature often do not map onto the mechanisms generating the probabilities. Our alternative conception of causal judgment is more congruent with both scientific uses of the notion of causation and observed causal judgments of untutored reasoners. We illustrate some of the relevant variables under this conception, using a framework for causal representation now widely adopted in computer science and, increasingly, in statistics. We also review the formulation and evidence for a theory of human causal induction (Cheng, 1997) that adopts this alternative conception. 1. The Old Mechanism Approach A long and still popular tradition in the study of human causal reasoning insists on a dramatic bifurcation between "mechanistic " conceptions of causalGlymour & Cheng inference and "probabilistic " or "covariational " conceptions of this process (e.g., Ahn

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 intuitive distinction between causal structure and causal strength: the difference between asking whether or not a causal relationship exists, and asking how strong that causal relationship might be. We show that the two leading rational models of elemental causal induction, #P and causal power, both estimate causal strength, and introduce a new rational model, causal support, that assesses causal structure. Causal support provides a better account of a large number of existing datasets than either #P or causal power. It also predicts several phenomena that cannot be accounted for by other models, which we explore through a series of experiments. These phenomena include the complex interaction between #P 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.