Causal learning : psychology, philosophy, and computation

PART I: CAUSATION AND INTERVENTION 1. Interventionist Theories of Causation in Psychological Perspective 2. Infants' Causal Learning: Intervention, Observation, Imitation 3. Detecting Causal Structure: The Role of Interventions in Infants' Understanding of Psychological and Physical Causal Relations 4. An Interventionist Approach to Causation in Psychology 5. Learning From Doing: Intervention and Causal Inference 6. Causal Reasoning Through Intervention 7. On the Importance of Causal Taxonomy PART II: CAUSATION AND PROBABILITY Introduction to Part II: Causation and Probability 8. Teaching the Normative Theory of Causal Reasoning 9. Interactions Between Causal and Statistical Learning 10. Beyond Covariation: Cues to Causal Structure 11. Theory Unification and Graphical Models in Human Categorization 12. Essentialism as a Generative Theory of Classification 13. Data-Mining Probalists or Experimental Determinists? A Dialogue on the Principles Underlying Causal Learning in Children 14. Learning the Structure of Deterministic Systems PART III: CAUSATION, THEORIES, AND MECHANISMS Introduction to Part III: Causation, Theories, and Mechanisms 15. Why Represent Causal Relations? 16. Causal Reasoning as Informed by the Early Development of Explanations 17. Dynamic Interpretations of Covariation Data 18. Statistical Jokes and Social Effects: Intervention and Invariance in Causal Relations 19. Intuitive Theories as Grammars for Causal Inference 20. Two Proposals for Causal Grammars