- Title Pages
- 1 Interventionist Theories of Causation in Psychological Perspective
- 2 Infants’ Causal Learning
- 3 Detecting Causal Structure
- 4 An Interventionist Approach to Causation in Psychology
- 5 Learning From Doing
- 6 Causal Reasoning Through Intervention
- 7 On the Importance of Causal Taxonomy
- Part II Causation and Probability
- 8 Teaching the Normative Theory of Causal Reasoning
- 9 Interactions Between Causal and Statistical Learning
- 10 Beyond Covariation
- 11 Theory Unification and Graphical Models in Human Categorization
- 12 Essentialism as a Generative Theory of Classification
- 13 Data-Mining Probabilists or Experimental Determinists?
- 14 Learning the Structure of Deterministic Systems
- 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
- 19 Intuitive Theories as Grammars for Causal Inference
- 20 Two Proposals for Causal Grammars
- (p.1) Introduction
- Causal Learning
- Oxford University Press
This chapter provides a simple, clear, and (hopefully) amusing introduction to causal model and Bayes nets theories in computer science, the interventionist account of causation in philosophy, and the psychology of causal learning in both adults and children. It takes the form of a fictional e-mail exchange between a developmental psychologist and a philosopher/computer scientist in which each partner explains the background of their field to the other. In two attachments, the fictional authors review the literature on causal Bayes nets and on the psychology of causal inference.
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