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On the Role of Counterfactuals in Inferring Causal Effects of Treatments

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  • Kluve, Jochen

    ()
    (Humboldt University Berlin, RWI)

Abstract

Causal inference in the empirical sciences is based on counterfactuals. This paper presents the counterfactual account of causation in terms of Lewis’s possible-world semantics, and reformulates the statistical potential outcome framework and its underlying assumptions using counterfactual conditionals. I discuss varieties of causally meaningful counterfactuals for the case of a finite number of treatments, and illustrate these using a simple set-theoretical framework. The paper proceeds to examine proximity relations between possible worlds, and discusses implications for empirical practice.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 354.

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Length: 57 pages
Date of creation: Sep 2001
Date of revision:
Handle: RePEc:iza:izadps:dp354

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Keywords: counterfactuals; possible worlds; treatment effect; Causation;

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References

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  1. James J. Heckman, 1989. "Choosing Among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training," NBER Working Papers 2861, National Bureau of Economic Research, Inc.
  2. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
  3. J.D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc.
  4. Goldberger, Arthur S, 1972. "Structural Equation Methods in the Social Sciences," Econometrica, Econometric Society, vol. 40(6), pages 979-1001, November.
  5. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097 Elsevier.
  6. Thomas Fraker & Rebecca Maynard, 1987. "The Adequacy of Comparison Group Designs for Evaluations of Employment-Related Programs," Journal of Human Resources, University of Wisconsin Press, vol. 22(2), pages 194-227.
  7. James J. Heckman, 1991. "Randomization and Social Policy Evaluation," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
  8. James J. Heckman, 2000. "Causal Parameters And Policy Analysis In Economics: A Twentieth Century Retrospective," The Quarterly Journal of Economics, MIT Press, vol. 115(1), pages 45-97, February.
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Cited by:
  1. Jochen Kluve & Boris Augurzky, 2005. "Assessing the performance of matching algorithms when selection into treatment is strong," RWI Discussion Papers 0021, Rheinisch-Westfälisches Institut für Wirtschaftsforschung.

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