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date: 13 November 2019

Abstract and Keywords

This chapter is an introduction to the psychology of causal inference using a computational perspective, with the focus on causal discovery. It explains the nature of the problem of causal discovery and illustrates the goal of the process with everyday and hypothetical examples. It reviews psychological research under two approaches to causal discovery, an associative approach and a causal approach that incorporates causal assumptions in the inference process. The latter approach provides a framework within which to answer different questions regarding causal inference coherently. The chapter ends with a consideration of causality as unfolding over time. We conclude with a sketch of future directions for the field.

Keywords: causal learning, rationality, associative models, causal models, causal bayes nets, bayesian causal models, temporal contiguity, causal invariance, intervention, empirical knowledge

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