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

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  • Thomas D. Cook

    (Professor of Sociology, Northwestern University)

  • William R. Shadish

    (Professor of Psychology, University of California, Merced)

  • Vivian C. Wong

    (Northwestern University)

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    Abstract

    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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    File URL: http://hdl.handle.net/10.1002/pam.20375
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    Bibliographic Info

    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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    Web page: http://www3.interscience.wiley.com/journal/34787/home

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    1. Jeffrey Smith & Petra Todd, 2003. "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?," University of Western Ontario, CIBC Centre for Human Capital and Productivity Working Papers 20035, University of Western Ontario, CIBC Centre for Human Capital and Productivity.
    2. 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.
    3. Steven Glazerman & Dan M. Levy & David Myers, 2003. "Nonexperimental Versus Experimental Estimates of Earnings Impacts," Mathematica Policy Research Reports 3694, Mathematica Policy Research.
    4. 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.
    5. 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.
    6. David McKenzie & John Gibson & Steven Stillman, 2010. "How Important Is Selection? Experimental vs. Non-Experimental Measures of the Income Gains from Migration," Journal of the European Economic Association, MIT Press, vol. 8(4), pages 913-945, 06.
    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. 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.
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    Cited by:
    1. Peter M. Steiner, 2011. "Propensity Score Methods for Causal Inference: On the Relative Importance of Covariate Selection, Reliable Measurement, and Choice of Propensity Score Technique," Working Papers 09, AlmaLaurea Inter-University Consortium.
    2. Henrik Hansen & Ninja Ritter Klejnstrup & Ole Winckler Andersen, 2011. "A Comparison of Model-based and Design-based Impact Evaluations of Interventions in Developing Countries," IFRO Working Paper 2011/16, University of Copenhagen, Department of Food and Resource Economics.
    3. Jeremy L. Hall, 2009. "Evidence-Based Practice and the Use of Information in State Agency Decision-Making," Working Papers 2009-10, University of Kentucky, Institute for Federalism and Intergovernmental Relations.
    4. Ron Zimmer & Brian Gill & Jonathon Attridge & Kaitlin Obenauf, 2013. "Charter School Authorizers and Student Achievement," Mathematica Policy Research Reports 7808, Mathematica Policy Research.
    5. Wanjala, Bernadette M. & Muradian, Roldan, 2013. "Can Big Push Interventions Take Small-Scale Farmers out of Poverty? Insights from the Sauri Millennium Village in Kenya," World Development, Elsevier, vol. 45(C), pages 147-160.
    6. Jiang, Miao & Foster, E. Michael & Gibson-Davis, Christina M., 2010. "The effect of WIC on breastfeeding: A new look at an established relationship," Children and Youth Services Review, Elsevier, vol. 32(2), pages 264-273, February.

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