Structure Learning in Human Causal Induction

摘要: 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 parameters a fixed graph. argue that complete account induction should also consider underlying graph structure, and we propose model this inductive process Bayesian inference. Our argument is supported through discussion three data sets.