Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
Joshua Tenenbaum, Thomas Griffiths
People routinely make sophisticated causal inferences unconsciously, ef- fortlessly, and from very little data – often from just one or a few ob- servations. We argue that these inferences can be explained as Bayesian computations over a hypothesis space of causal graphical models, shaped by strong top-down prior knowledge in the form of intuitive theories. We present two case studies of our approach, including quantitative mod- els of human causal judgments and brief comparisons with traditional bottom-up models of inference.