IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/27738.html
   My bibliography  Save this paper

Using a Satisficing Model of Experimenter Decision-Making to Guide Finite-Sample Inference for Compromised Experiments

Author

Listed:
  • Ganesh Karapakula
  • James J. Heckman

Abstract

This paper presents a simple decision-theoretic economic approach for analyzing 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 favorable) randomization null distributions. Our approach accommodates a variety of compromised experiments, including imperfectly documented re-randomization designs. To make our analysis concrete, we focus much of our discussion on the influential Perry Preschool Project. We reexamine previous estimates of program effectiveness using our methods. The choice of how to model reassignment vitally affects inference.

Suggested Citation

  • Ganesh Karapakula & James J. Heckman, 2020. "Using a Satisficing Model of Experimenter Decision-Making to Guide Finite-Sample Inference for Compromised Experiments," NBER Working Papers 27738, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:27738
    Note: CH TWP
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w27738.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Cattaneo, Matias D., 2010. "Efficient semiparametric estimation of multi-valued treatment effects under ignorability," Journal of Econometrics, Elsevier, vol. 155(2), pages 138-154, April.
    3. 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.
    4. Chung, EunYi & Romano, Joseph P., 2016. "Multivariate and multiple permutation tests," Journal of Econometrics, Elsevier, vol. 193(1), pages 76-91.
    5. Matias D. Cattaneo, 2010. "multi-valued treatment effects," The New Palgrave Dictionary of Economics,, Palgrave Macmillan.
    6. 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.
    7. 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.
    8. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    9. Miriam Bruhn & David McKenzie, 2009. "In Pursuit of Balance: Randomization in Practice in Development Field Experiments," American Economic Journal: Applied Economics, American Economic Association, vol. 1(4), pages 200-232, October.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2020. "Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis," Econometrica, Econometric Society, vol. 88(1), pages 265-296, January.
    15. Joseph P. Romano & Azeem M. Shaikh & Michael Wolf, 2010. "Hypothesis Testing in Econometrics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 75-104, September.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. James J. Heckman & Ganesh Karapakula, 2019. "The Perry Preschoolers at Late Midlife: A Study in Design-Specific Inference," Working Papers 2019-034, Human Capital and Economic Opportunity Working Group.
    2. Jeffrey D. Michler & Anna Josephson, 2022. "Recent developments in inference: practicalities for applied economics," Chapters, in: A Modern Guide to Food Economics, chapter 11, pages 235-268, Edward Elgar Publishing.
    3. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    4. Orla Doyle & Colm Harmon & James J. Heckman & Caitriona Logue & Seong Moon, 2013. "Measuring Investment in Human Capital Formation: An Experimental Analysis of Early Life Outcomes," NBER Working Papers 19316, National Bureau of Economic Research, Inc.
    5. Cortés, Darwin & Maldonado, Darío & Gallego, Juan & Charpak, Nathalie & Tessier, Rejean & Ruiz, Juan Gabriel & Hernandez, José Tiberio & Uriza, Felipe & Pico, Julieth, 2022. "Comparing long-term educational effects of two early childhood health interventions," Journal of Health Economics, Elsevier, vol. 86(C).
    6. John A. List & Azeem M. Shaikh & Yang Xu, 2019. "Multiple hypothesis testing in experimental economics," Experimental Economics, Springer;Economic Science Association, vol. 22(4), pages 773-793, December.
    7. John A. List & Azeem M. Shaikh & Atom Vayalinkal, 2023. "Multiple testing with covariate adjustment in experimental economics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 920-939, September.
    8. 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.
    9. Ke Zhu & Hanzhong Liu, 2023. "Pair‐switching rerandomization," Biometrics, The International Biometric Society, vol. 79(3), pages 2127-2142, September.
    10. Orla Doyle, 2017. "The First 2,000 Days and Child Skills: Evidence from a Randomized Experiment of Home Visiting," Working Papers 201715, School of Economics, University College Dublin.
    11. 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.
    12. Laurent Davezies & Guillaume Hollard & Pedro Vergara Merino, 2024. "Revisiting Randomization with the Cube Method," Papers 2407.13613, arXiv.org.
    13. Yingfei Mu & Edward A. Rubin & Eric Zou, 2021. "What’s Missing in Environmental (Self-)Monitoring: Evidence from Strategic Shutdowns of Pollution Monitors," NBER Working Papers 28735, National Bureau of Economic Research, Inc.
    14. Doyle, Orla & Harmon, Colm & Heckman, James J. & Logue, Caitriona & Moon, Seong Hyeok, 2017. "Early skill formation and the efficiency of parental investment: A randomized controlled trial of home visiting," Labour Economics, Elsevier, vol. 45(C), pages 40-58.
    15. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    16. Orazio Attanasio & Sarah Cattan & Emla Fitzsimons & Costas Meghir & Marta Rubio-Codina, 2020. "Estimating the Production Function for Human Capital: Results from a Randomized Controlled Trial in Colombia," American Economic Review, American Economic Association, vol. 110(1), pages 48-85, January.
    17. Fabian Kosse & Thomas Deckers & Pia Pinger & Hannah Schildberg-Hörisch & Armin Falk, 2020. "The Formation of Prosociality: Causal Evidence on the Role of Social Environment," Journal of Political Economy, University of Chicago Press, vol. 128(2), pages 434-467.
    18. Attanasio, Orazio & Cattan, Sarah & Fitzsimons, Emla & Meghir, Costas & Rubio-Codina, Marta, 2015. "Estimating the Production Function for Human Capital: Results from a Randomized Control Trial in Colombia," IZA Discussion Papers 8856, Institute of Labor Economics (IZA).
    19. Gabriella Conti & Christopher Hansman & James J. Heckman & Matthew F. X. Novak & Angela Ruggiero & Stephen J. Suomi, 2012. "Primate Evidence on the Late Health Effects of Early Life Adversity," Working Papers 2012-008, Human Capital and Economic Opportunity Working Group.
    20. Picchio, Matteo & van Ours, Jan C., 2024. "The impact of high temperatures on performance in work-related activities," Labour Economics, Elsevier, vol. 87(C).

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:27738. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.