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Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within-study comparisons

  • Thomas D. Cook

    (Professor of Sociology, Northwestern University)

  • William R. Shadish

    (Professor of Psychology, University of California, Merced)

  • Vivian C. Wong

    (Northwestern University)

Registered author(s):

    This paper analyzes 12 recent within-study comparisons contrasting causal estimates from a randomized experiment with those from an observational study sharing the same treatment group. The aim is to test whether different causal estimates result when a counterfactual group is formed, either with or without random assignment, and when statistical adjustments for selection are made in the group from which random assignment is absent. We identify three studies comparing experiments and regression-discontinuity (RD) studies. They produce quite comparable causal estimates at points around the RD cutoff. We identify three other studies where the quasi-experiment involves careful intact group matching on the pretest. Despite the logical possibility of hidden bias in this instance, all three cases also reproduce their experimental estimates, especially if the match is geographically local. We then identify two studies where the treatment and nonrandomized comparison groups manifestly differ at pretest but where the selection process into treatment is completely or very plausibly known. Here too, experimental results are recreated. Two of the remaining studies result in correspondent experimental and nonexperimental results under some circumstances but not others, while two others produce different experimental and nonexperimental estimates, though in each case the observational study was poorly designed and analyzed. Such evidence is more promising than what was achieved in past within-study comparisons, most involving job training. Reasons for this difference are discussed. © 2008 by the Association for Public Policy Analysis and Management.

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    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Policy Analysis and Management.

    Volume (Year): 27 (2008)
    Issue (Month): 4 ()
    Pages: 724-750

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    Handle: RePEc:wly:jpamgt:v:27:y:2008:i:4:p:724-750
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    1. McKenzie, David & Gibson, John & Stillman, Steven, 2006. "How Important Is Selection? Experimental vs. Non-Experimental Measures of the Income Gains from Migration," IZA Discussion Papers 2087, Institute for the Study of Labor (IZA).
    2. Steven Glazerman Dan Levy David Myers, 2003. "Nonexperimental Versus Experimental Estimates of Earnings Impacts," Mathematica Policy Research Reports 7c8bd68ac8db47caa57c70ee1, Mathematica Policy Research.
    3. James J. Heckman & Jeffrey A. Smith, 1995. "Assessing the Case for Social Experiments," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 85-110, Spring.
    4. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, vol. 65(2), pages 261-94, April.
    5. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    6. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-20, September.
    7. Roberto Agodini & Mark Dynarski, 2004. "Are Experiments the Only Option? A Look at Dropout Prevention Programs," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 180-194, February.
    8. repec:mpr:mprres:3694 is not listed on IDEAS
    9. Cook, Thomas D., 2008. ""Waiting for Life to Arrive": A history of the regression-discontinuity design in Psychology, Statistics and Economics," Journal of Econometrics, Elsevier, vol. 142(2), pages 636-654, February.
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