Abstract
According to the causal power view, two core constraints—that causes occur independently (i.e., no confounding) and influence their effects independently—serve as boundary conditions for causal induction. This study investigated how violations of these constraints modulate uncertainty about the existence and strength of a causal relationship. Participants were presented with pairs of candidate causes that were either confounded or not, and that either interacted or exerted their influences independently. Consistent with the causal power view, uncertainty about the existence and strength of causal relationships was greater when causes were confounded or interacted than when unconfounded and acting independently. An elemental Bayesian causal model captured differences in uncertainty due to confounding but not those due to an interaction. Implications of distinct sources of uncertainty for the selection of contingency information and causal generalization are discussed.
Highlights
How do multiple causes combine to influence their effects? According to the causal power view (Cartwright, 1989; Cheng, 1997), learners apply a set of generic, a priori, constraints that enable the “teasing apart” of individual causal influences
Confounded Causes Structure Judgments Consistent with the causal power view, as can be seen in Figure 3, both human judgments and the Bayesian causal model reflect high levels of uncertainty associated with confounded target cause C in Phase 1; uncertainty that is subsequently resolved by individual presentations of that cause either with or without the effect in Phase 2
This study investigated the influence of violations of causal power assumptions on uncertainty about the existence and strength of a causal relationship
Summary
How do multiple causes combine to influence their effects? According to the causal power view (Cartwright, 1989; Cheng, 1997), learners apply a set of generic, a priori, constraints that enable the “teasing apart” of individual causal influences. A probabilistic formalization of the causal power view, the power-PC theory (Cheng, 1997), mathematically defines the problem with estimating the strength of confounded causes: Whenever the probability of the occurrence of one candidate cause (e.g., Y) differs across the absence and presence of another (e.g., X), the power-PC equations contain multiple unknowns so that there is no unique solution. The present study extends Griffiths and Tenenbaum’s (Tenenbaum and Griffiths, 2001; Griffiths and Tenenbaum, 2005) Bayesian causal model to the case of two candidate causes and uses this extension as a normative framework for assessing how violations of causal power assumptions influence uncertainty in causal inference
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