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Related Concept Videos

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Mechanistic Models: Overview of Compartment Models

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Related Experiment Videos

Compact representations of extended causal models.

Joseph Y Halpern1, Christopher Hitchcock

  • 1Computer Science Department, Cornell University, Ithaca, NY 14853, USA. halpern@cs.cornell.edu

Cognitive Science
|July 20, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a compact representation for extended causal models, simplifying the complex definition of actual causation that combines causal structure and normality considerations.

Keywords:
Bayesian networkCausalityCompact representationNormalTypical

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Area of Science:

  • Artificial Intelligence
  • Philosophy of Science

Background:

  • Judea Pearl's 2000 work defined actual causation using causal models.
  • Subsequent research indicates that normality considerations are crucial for adequate causal accounts.
  • Extended causal models integrate causal structure with normality but can be complex.

Purpose of the Study:

  • To develop a method for achieving a compact representation of extended causal models.
  • To simplify the application of causal models that include normality.

Main Methods:

  • The study focuses on representing extended causal models more efficiently.
  • It explores techniques for achieving compactness without sacrificing essential information.

Main Results:

  • A method for creating compact representations of extended causal models has been demonstrated.
  • This approach reduces the complexity associated with incorporating normality into causal analysis.

Conclusions:

  • Compact representations of extended causal models are feasible.
  • This simplification facilitates a more practical and accessible approach to understanding actual causation.