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On the Role of Counterfactuals in Inferring Causal Effects of Treatments

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  • Kluve, Jochen

    (KfW Development Bank)

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.

Suggested Citation

  • Kluve, Jochen, 2001. "On the Role of Counterfactuals in Inferring Causal Effects of Treatments," IZA Discussion Papers 354, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp354
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    References listed on IDEAS

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    1. James J. Heckman, 1991. "Randomization and Social Policy Evaluation Revisited," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
    2. 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.
    3. Heckman, J.J. & Hotz, V.J., 1988. "Choosing Among Alternative Nonexperimental Methods For Estimating The Impact Of Social Programs: The Case Of Manpower Training," University of Chicago - Economics Research Center 88-12, Chicago - Economics Research Center.
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    5. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    6. James J. Heckman, 2000. "Causal Parameters and Policy Analysis in Economics: A Twentieth Century Retrospective," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(1), pages 45-97.
    7. Joshua 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.
    8. 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.
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    Cited by:

    1. Jochen Kluve & Boris Augurzky, 2007. "Assessing the performance of matching algorithms when selection into treatment is strong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 533-557.
    2. Daniel L. Millimet & John A. List, 2003. "A Natural Experiment on the ‘Race to the Bottom’ Hypothesis: Testing for Stochastic Dominance in Temporal Pollution Trends," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(4), pages 395-420, September.
    3. Fertig, Michael, 2002. "Evaluating Immigration Policy Potentials and Limitations," IZA Discussion Papers 437, Institute of Labor Economics (IZA).
    4. Włodzimierz Okrasa, 2012. "Statistics and Sociology: The mutually-supportive development from the perspective of interdisciplinarization of social research," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(2), pages 365-386, June.

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    More about this item

    Keywords

    treatment effect; possible worlds; counterfactuals; Causation;
    All these keywords.

    JEL classification:

    • B30 - Schools of Economic Thought and Methodology - - History of Economic Thought: Individuals - - - General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • Z00 - Other Special Topics - - General - - - General

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