Causal Learning: Psychology, Philosophy, and Computation

Introduction. Allison Gopnik and Laura Schulz Part I. Causation and Intervention 2. Infants' Causal Learning: Intervention, Observation, Imitation, Andrew N. Meltzoff 3. Detecting Causal Structure: The Role of Intervention in Infants' Understanding of Psychological and Physical Causal Relations, Jessica A. Sommerville 4. An Interventionist Approach to Causation in Psychology, John Campbell 5. Learning From Doing: Intervention and Causal Inference, Laura Schulz, Tamar Kushnir, and Alison Gopnik 6. Casual Reasoning Through Intervention, York Hagmayer, Steven Sloman, David Lagnado, and Michael R. Waldmann 7. On the Importance of Causal Taxonomy, Christopher Hitchcock Part II: Causation and Probability. Introduction to Part II. Alison Gopnik and Laura Schulz 8. Teaching the Normative Theory of Casual Reasoning, Richard Scheines, Matt Easterday, and David Danks 9. Interactions Between Causal and Statistical Learning, David M. Sobel and Natasha Z. Kirkham 10. Beyond Covariation: Cues to Causal Structure, David A. Lagnado, Michael R. Waldmann, York Hagmayer, and Steven A. Sloman 11. Theory Unification and Graphical Models in Human Categorization, David Danks 12. Essential as a Generative Theory of Classification, Bob Rehder 13. Data-mining Probabilists or Experimental Determinists?: A Dialogue on the Principles Underlying Causal Learning in Children, Thomas Richardson, Laura Schultz, and Alison Gopnik 14. Learning the Structure of Deterministic Systems, Clark Glymour Part III: Causation, Theories and Mechanisms. Introduction to Part III. Alison Gopnik and Laura Schulz 15. Why Represent Causal Relations?, Michael Strevens 16. Causal Reasoning as Informed by the Early Development of Explanations, Henry M. Wellman and David Liu 17. Dynamic Interpretations of Covariation Data, Woo-kyoung Ahn, Jessecae K. Marsh, and Christian C. Luhmann 18. Statistical Jokes and Social Effects: Intervention and Invariance in Causal Relations, Clark Glymour 19. Intuitive Theories as Grammars for Causal Inference, Joshua B. Tenenbaum, Thomas L. Griffiths, and Sourabh Niyogi 20. Two Proposals for Causal Grammars, Thomas L. Griffiths and Joshua B. Tenenbaum Notes. Index.

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Nyckelord: Intervention, Observation, Imitation, Psychological