Structure Learning in Human Causal Induction

Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)

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Joshua Tenenbaum, Thomas Griffiths


We use graphical models to explore the question of how people learn sim(cid:173) ple causal relationships from data. The two leading psychological theo(cid:173) ries can both be seen as estimating the parameters of a fixed graph. We argue that a complete account of causal induction should also consider how people learn the underlying causal graph structure, and we propose to model this inductive process as a Bayesian inference. Our argument is supported through the discussion of three data sets.