Causal Models and Counterfactuals

  • James MahoneyEmail author
  • Gary Goertz
  • Charles C. Ragin
Part of the Handbooks of Sociology and Social Research book series (HSSR)


This article compares statistical and set-theoretic approaches to causal analysis. Statistical researchers commonly use additive, linear causal models, whereas set-theoretic researchers typically use logic-based causal models. These models differ in many fundamental ways, including whether they assume symmetric or asymmetrical causal patterns, and whether they call attention to equifinality and combinatorial causation. The two approaches also differ in how they utilize counterfactuals and carry out counterfactual analysis. Statistical researchers use counterfactuals to illustrate their results, but they do not use counterfactual analysis for the goal of causal model estimation. By contrast, set-theoretic researchers use counterfactuals to estimate models by making explicit their assumptions about empty sectors in the vector space defined by the causal variables. The paper concludes by urging greater appreciation of the differences between the statistical and set-theoretic approaches to causal analysis.


Causal Model Causal Variable Qualitative Comparative Analysis Strong Union Causal Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Stephen Morgan and Judea Pearl for helpful comments on a previous version.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • James Mahoney
    • 1
    Email author
  • Gary Goertz
    • 2
  • Charles C. Ragin
    • 3
    • 4
  1. 1.Departments of Political Science and SociologyNorthwestern UniversityEvanstonUSA
  2. 2.Kroc Institute for International Peace StudiesUniversity of Notre DameSouth BendUSA
  3. 3.Department of SociologyUniversity of CaliforniaIrvineUSA
  4. 4.University of Southern DenmarkOdenseDenmark

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