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Inference with Imperfect Randomization: The Case of the Perry Preschool Program


  • James J. Heckman
  • Rodrigo Pinto
  • Azeem M. Shaikh
  • Adam Yavitz


This paper considers the problem of making inferences about the effects of a program on multiple outcomes when the assignment of treatment status is imperfectly randomized. By imperfect randomization we mean that treatment status is reassigned after an initial randomization on the basis of characteristics that may be observed or unobserved by the analyst. We develop a partial identification approach to this problem that makes use of information limiting the extent to which randomization is imperfect to show that it is still possible to make nontrivial inferences about the effects of the program in such settings. We consider a family of null hypotheses in which each null hypothesis specifies that the program has no effect on one of several outcomes of interest. Under weak assumptions, we construct a procedure for testing this family of null hypotheses in a way that controls the familywise error rate -- the probability of even one false rejection -- in finite samples. We develop our methodology in the context of a reanalysis of the HighScope Perry Preschool program. We find statistically significant effects of the program on a number of different outcomes of interest, including outcomes related to criminal activity for males and females, even after accounting for the imperfectness of the randomization and the multiplicity of null hypotheses.

Suggested Citation

  • James J. Heckman & Rodrigo Pinto & Azeem M. Shaikh & Adam Yavitz, 2011. "Inference with Imperfect Randomization: The Case of the Perry Preschool Program," NBER Working Papers 16935, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:16935
    Note: CH TWP

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    Cited by:

    1. Federico A. Bugni & Ivan A. Canay & Azeem M. Shaikh, 2015. "Inference under covariate-adaptive randomization," CeMMAP working papers CWP45/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Aviva Aron-Dine & Liran Einav & Amy Finkelstein, 2013. "The RAND Health Insurance Experiment, Three Decades Later," Journal of Economic Perspectives, American Economic Association, vol. 27(1), pages 197-222, Winter.
    3. Daniela Del Boca & Christopher Flinn & Matthew Wiswall, 2012. "Transfers to Households with Children and Child Development," Carlo Alberto Notebooks 273, Collegio Carlo Alberto.
    4. John List & Azeem Shaikh & Yang Xu, 2016. "Multiple Hypothesis Testing in Experimental Economics," Artefactual Field Experiments 00402, The Field Experiments Website.
    5. Asoni, Andrea, 2011. "Intelligence, Self-confidence and Entrepreneurship," Working Paper Series 887, Research Institute of Industrial Economics.
    6. Filatriau, Olivier & Fougère, Denis & Tô, Maxime, 2013. "Will Sooner Be Better? The Impact of Early Preschool Enrollment on Cognitive and Noncognitive Achievement of Children," CEPR Discussion Papers 9480, C.E.P.R. Discussion Papers.

    More about this item

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth

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