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Related Experiment Videos

Covariation in natural causal induction.

P W Cheng1, L R Novick

  • 1Department of Psychology, University of California, Los Angeles 90024-1563.

Psychological Review
|April 1, 1992
PubMed
Summary
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Causal inference relies on understanding how events covary. This study reveals a single, normative mechanism—probabilistic contrast computation—underlies everyday causal induction, challenging prior views of its heterogeneity.

Area of Science:

  • Cognitive Psychology
  • Social Psychology
  • Philosophy of Science

Background:

  • Covariation is central to causal inference in psychology and philosophy.
  • Previous models describe this process as heterogeneous and biased.
  • Focal sets are contextually determined event sets for covariation computation.

Purpose of the Study:

  • To analyze existing models of covariation computation in causal inference.
  • To compare these models with a probabilistic contrast model.
  • To identify a unified normative mechanism for causal induction.

Main Methods:

  • Analysis of existing psychological and philosophical models of covariation.
  • Examination of the role of focal sets in causal inference.
  • Comparison of empirical models with a normative probabilistic contrast model.

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Main Results:

  • Covariation computation is not inherently heterogeneous or biased.
  • A single mechanism, probabilistic contrast computation, underlies causal induction.
  • This mechanism applies to both everyday and scientific causal reasoning.

Conclusions:

  • The computation of probabilistic contrasts provides a unified account of covariation in causal inference.
  • This normative mechanism challenges previous conceptions of causal induction as biased.
  • A consistent framework for understanding natural causal induction is proposed.