A Computational Theory of Learning Causal Relationships

I present a cognitive model of the human ability to acquire causal relationships. I report on experimental evidence demonstrating that human learners acquire accurate causal relationships more rapidly when training examples are consistent with a general theory of causality. This article describes a learning process that uses a general theory of causality as background knowledge. The learning process, which I call theory-driven learning (TDL), hypothesizes causal relationships consistent both with observed data and the general theory of causality. TDL accounts for data on both the rate at which human learners acquire causal relationships, and the types of causal relationships they acquire. Experiments with TDL demonstrate the advantage of TDL for acquiring causal relationships over similarity-based approaches to learning: Fewer examples are required to learn an accurate relationship.