图书简介
Causal Learning provides a compendium of research determining how, in principle, the problem of causal inference and learning can be solved, and a wealth of methods for determining how it is, in fact, solved by children, adults, and animals.
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
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