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Using a satisficing model of experimenter decision-making to guide finite-sample inference for compromised experiments

Author

Listed:
  • James J Heckman
  • Ganesh Karapakula
Abstract
SummaryThis paper presents a simple decision-theoretic economic approach for analysing social experiments with compromised random assignment protocols that are only partially documented. We model administratively constrained experimenters who satisfice in seeking covariate balance. We develop design-based small-sample hypothesis tests that use worst-case (least favourable) randomization null distributions. Our approach accommodates a variety of compromised experiments, including imperfectly documented rerandomization designs. To make our analysis concrete, we focus much of our discussion on the influential Perry Preschool Project. We reexamine previous estimates of programme effectiveness using our methods. The choice of how to model reassignment vitally affects inference.

Suggested Citation

  • James J Heckman & Ganesh Karapakula, 2021. "Using a satisficing model of experimenter decision-making to guide finite-sample inference for compromised experiments," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 1-39.
    • Handle: RePEc:oup:emjrnl:v:24:y:2021:i:2:p:c1-c39.
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    File URL: http://hdl.handle.net/10.1093/ectj/utab009
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    1. James Heckman & Seong Hyeok Moon & Rodrigo Pinto & Peter Savelyev & Adam Yavitz, 2010. "Analyzing social experiments as implemented: evidence from the HighScope Perry Preschool Program," CeMMAP working papers CWP22/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Kari Lock Morgan & Donald B. Rubin, 2015. "Rerandomization to Balance Tiers of Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1412-1421, December.
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    5. Rodrigo Pinto & Azeem Shaikh & Adam Yavitz & James Heckman, 2010. "Inference with Imperfect Randomization: The Case of the Perry Preschool Program," 2010 Meeting Papers 1336, Society for Economic Dynamics.
    6. Joseph P. Romano & Michael Wolf, 2005. "Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 94-108, March.
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    9. Matias D. Cattaneo, 2010. "multi-valued treatment effects," The New Palgrave Dictionary of Economics,, Palgrave Macmillan.
    10. James Heckman & Seong Hyeok Moon & Rodrigo Pinto & Peter Savelyev & Adam Yavitz, 2010. "Analyzing social experiments as implemented: A reexamination of the evidence from the HighScope Perry Preschool Program," Quantitative Economics, Econometric Society, vol. 1(1), pages 1-46, July.
    11. Joseph P. Romano & Azeem M. Shaikh, 2010. "Inference for the Identified Set in Partially Identified Econometric Models," Econometrica, Econometric Society, vol. 78(1), pages 169-211, January.
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    14. Cunha, Flavio & Heckman, James J. & Lochner, Lance, 2006. "Interpreting the Evidence on Life Cycle Skill Formation," Handbook of the Economics of Education, in: Erik Hanushek & F. Welch (ed.), Handbook of the Economics of Education, edition 1, volume 1, chapter 12, pages 697-812, Elsevier.
    15. Young, Alwyn, 2019. "Channeling Fisher: randomization tests and the statistical insignificance of seemingly significant experimental results," LSE Research Online Documents on Economics 101401, London School of Economics and Political Science, LSE Library.
    16. Alwyn Young, 2019. "Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(2), pages 557-598.
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    Cited by:

    1. Zhao, Anqi & Ding, Peng, 2021. "Covariate-adjusted Fisher randomization tests for the average treatment effect," Journal of Econometrics, Elsevier, vol. 225(2), pages 278-294.

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    More about this item

    Keywords

    Randomized controlled trial; rerandomization; satisficing; worst-case randomization tests; partial identification; design-based least favourable small-sample inference;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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