The present paper examines a type of abstract domain-general knowledge required for the process of constructing useable domain-specific causal knowledge, the evident goal of causal learning. It tests the hypothesis that analytic knowledge of "causal-invariance decomposition functions" is essential for this process. Such knowledge…

An integration of two different approaches to the psychology of causal induction is proposed that overcomes the problems associated with each. The proposal results in a causal power theory of the probabilistic contrast model of P. W. Cheng and L. R. Novick (1990). (SLD)

Proposes a coherence hypothesis that integrates both the "power" and "covariational" views of how people distinguish genuine causes from spurious ones. Results of 2 experiments involving 96 and 56 undergraduates support the hypothesis. Discusses the place of coherence within causal inference. (SLD)

The task of causal learning concerns figuring out the laws that govern how the world works. The goal of a reasoner who engages in this task is to gain an understanding of the empirical world that would guide decisions regarding actions to achieve the reasoner's objectives. The comments by P. A. White and C. Luhmann and W.-k. Ahn on P. W. Cheng and…

Biases and models usually offered by cognitive and social psychology and by philosophy to explain causal induction are evaluated with respect to focal sets (contextually determined sets of events over which covariation is computed). A probabilistic contrast model is proposed as underlying covariation computation in natural causal induction. (SLD)