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Open Access Publications from the University of California

Causal invariance guides inference of empirical integration rules


The present paper reports an experiment (N=254) testing two views of how reasoners learn and generalize potentially complex causal knowledge. Previous work has focused on reasoners’ ability to learn rules describing how pre-defined candidate causes combine, potentially interactively, to produce an outcome in a domain. This empirical-function learning view predicts that reasoners would generalize an acquired combination rule based on similarity to stimuli they experienced in the domain. An alternative causal-invariance view goes beyond empirical learning: it allows for the possibility that one’s current representation may not yield useable (i.e., invariant) causal knowledge –– knowledge that holds true when applied. Accordingly, because useable causal knowledge is the evident aspiration of causal induction, this view posits that deviation from causal invariance is a criterion for knowledge revision. The criterion shapes the empirical functions learned and retained. A discriminating test is whether reasoners would re-represent interacting causes as a whole cause that does not interact with other causes, even when in their relevant experience all (pre-defined) causes in the domain interact. Our results favor the causal-invariance view.

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