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  1. Alison Gopnik, Clark Glymour, David M. Sobel & Laura E. Schultz, Causal Learning in Children: Causal Maps and Bayes Nets.
    We outline a cognitive and computational account of causal learning in children. We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent representation of the causal relations among events. This kind of knowledge can be perspicuously represented by the formalism of directed graphical causal models, or “Bayes nets”. Human causal learning and inference may involve computations similar to those for learnig causal Bayes nets and for predicting with (...)
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  2. Christopher D. Erb, David W. Buchanan & David M. Sobel (2013). Children's Developing Understanding of the Relation Between Variable Causal Efficacy and Mechanistic Complexity. Cognition 129 (3):494-500.
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  3. Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik (2011). Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults. Cognitive Science 35 (8):1407-1455.
    People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which (...)
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  4. David M. Sobel (2011). Knowledge and Children's Reasoning About Possibility. In Christoph Hoerl, Teresa McCormack & Sarah R. Beck (eds.), Understanding Counterfactuals, Understanding Causation. Oxford University Press. 123.
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  5. David M. Sobel (2009). Enabling Conditions and Children's Understanding of Pretense. Cognition 113 (2):177-188.
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  6. David W. Buchanan & David M. Sobel (2008). Bridging the Gap: Children's Developing Inferences About Objects' Labels and Insides From Causality-at-a-Distance. In. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. 64--70.
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  7. David M. Sobel & Natasha Z. Kirkham (2007). Interactions Between Causal and Statistical Learning. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. 139--153.
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  8. Alison Gopnik, Clark Glymour, David M. Sobel, Laura Schulz, Tamar Kushnir & David Danks, A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.
    We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...)
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  9. Clark Glymour, Alison Gopnik, David M. Sobel & Laura E. Schulz, Causal Learning Mechanisms in Very Young Children: Two-, Three-, and Four-Year-Olds Infer Causal Relations From Patterns of Variation and Covariation.
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