Learning Complex Causal Structures
Most current theories of human causal learning are essentially parameter estimators: they assume a fixed causal structure and estimate causal strengths within that structure. In these theories, absence of causation is represented as zero causal strength, rather than a distinct causal structure. In this paper, we first present the theoretical framework of Bayesian networks, which can represent both structure (presence/absence of causation) and parameters (strength of causation). We then present a series of experiments involving a particularly complex causal structure and a novel methodology that focuses on structural discriminations, rather than parameter estimation. These experiments suggest that people are capable of doing more than just parameter estimation. A significant group of participants seems to be learning (something isomorphic to) a Bayesian