Structure Learning in Human Causal Induction

We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories 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.

• Type: Conference
• Year: 2000
• Where: NIPS
• Authors: Joshua B. Tenenbaum, Thomas L. Griffiths
• Keywords: Causal Graph Structure, Leading Psychological Theories, NIPS 2000, NIPS 2007, Simple Causal Relationships