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date: 27 May 2020

Abstract and Keywords

Causal knowledge is central to children’s understanding of the world, but there have been many different conceptions of how causal knowledge is represented and how it is learned. I outline four different approaches to causality that have influenced developmental research, stressing agency, mechanism, association, and probabilistic models, and give examples of theory and research in each area. I focus in more detail on the most recent probabilistic model accounts that stress the role of causation in inferences about possibilities. These include potential interventions to change the world and counterfactuals about alternate ways the world might be. These accounts also employ computational ideas about causal Bayes nets and Bayesian learning, and these ideas are outlined.

Keywords: causal learning, conceptual development, intuitive theories, probabilistic models, causal Bayes nets, Bayesian learning

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