The Mind's Arrows

Bayes Nets and Graphical Causal Models in Psychology

In recent years, small groups of statisticians, computer scientists, and philosophers have developed an account of how partial causal knowledge can be used to compute the effect of actions and how causal relations can be learned, at least by computers. The representations used in the emerging theory are causal Bayes nets or graphical causal models.

In his new book, Clark Glymour provides an informal introduction to the basic assumptions, algorithms, and techniques of causal Bayes nets and graphical causal models in the context of psychological examples. He demonstrates their potential as a powerful tool for guiding experimental inquiry and for interpreting results in developmental psychology, cognitive neuropsychology, psychometrics, social psychology, and studies of adult judgment. Using Bayes net techniques, Glymour suggests novel experiments to distinguish among theories of human causal learning and reanalyzes various experimental results that have been interpreted or misinterpreted—without the benefit of Bayes nets and graphical causal models. The capstone illustration is an analysis of the methods used in Herrnstein and Murray’s book The Bell Curve; Glymour argues that new, more reliable methods of data analysis, based on Bayes nets representations, would lead to very different conclusions from those advocated by Herrnstein and Murray.

Table of Contents

  1. Acknowledgments
  2. 1. Introduction
  3. I. Developmental Psychology and Discover
  4. 2. Android Epistemology for Babies
  5. 3. Another Way for Nerds to Make Babies: The Frame Problem and Causal Inference in Developmental Psychology
  6. II. Adult Judgements of Causation
  7. 4. A Puzzling Experiment
  8. 5. The Puzzle Resolved
  9. 6. Marilyn vos Savant Meets Rescorla and Wagner
  10. 7. Cheng Models
  11. 8. Learning Procedures
  12. 9. Representations and Rationality: The Case of Backward Blocking
  13. III. Inference and Explanation in Cognitive Neuropsychology
  14. 10. Cognitive Parts: From Freud to Farah
  15. 11. Inferences to Cognitive Architecture from Individual Case Studies
  16. 12. Group Data in Cognitive Neuropsychology
  17. 13. The Explanatory Power of Lesioning Neural Nets
  18. IV. Psychometrics and Social Psychology
  19. 14. Social Statistics and Genuine Inquiry: The Case of
  20. Notes
  21. References
  22. Index