Minds and Machines

, Volume 8, Issue 1, pp 39–60

Learning Causes: Psychological Explanations of Causal Explanation1

  • Clark Glymour

DOI: 10.1023/A:1008234330618

Cite this article as:
Glymour, C. Minds and Machines (1998) 8: 39. doi:10.1023/A:1008234330618


I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directed graphs that encode conditional independence (screening off) relations. I illustrate the benefits of that representation, now widely used in computer science and increasingly in statistics, by (i) showing that a dispute in psychology between ‘mechanist’ and ‘associationist’ psychological theories of causation rests on a false and confused dichotomy; (ii) showing that a recent, much-cited experiment, purporting to show that human subjects, incorrectly let large causes ‘overshadow’ small causes, misrepresents the most likely, and warranted, causal explanation available to the subjects, in the light of which their responses were normative; (iii) showing how a recent psychological theory (due to P. Cheng) of human judgment of causal power can be considerably generalized: and (iv) suggesting a range of possible experiments comparing human and computer abilities to extract causal information from associations.

causecausationdirected graphsexplanationjudgmentunder certainty

Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Clark Glymour
    • 1
  1. 1.University of California at San Diego and Carnegie Mellon UniversityUSA