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Incentive Compatible Estimators

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  • Eliaz, Kfir
  • Spiegler, Ran

Abstract

We study a model in which a "statistician" takes an action on behalf of an agent, based on a random sample involving other people. The statistician follows a penalized regression procedure: the action that he takes is the dependent variable's estimated value given the agent's disclosed personal characteristics. We ask the following question: Is truth-telling an optimal disclosure strategy for the agent, given the statistician's procedure? We discuss possible implications of our exercise for the growing reliance on "machine learning" methods that involve explicit variable selection.

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  • Eliaz, Kfir & Spiegler, Ran, 2018. "Incentive Compatible Estimators," CEPR Discussion Papers 12804, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:12804
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    References listed on IDEAS

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    1. Perote, Javier & Perote-Pena, Juan, 2004. "Strategy-proof estimators for simple regression," Mathematical Social Sciences, Elsevier, vol. 47(2), pages 153-176, March.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    3. Sylvain Chassang & Gerard Padro I Miquel & Erik Snowberg, 2012. "Selective Trials: A Principal-Agent Approach to Randomized Controlled Experiments," American Economic Review, American Economic Association, vol. 102(4), pages 1279-1309, June.
    4. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    5. Xavier Gabaix, 2014. "A Sparsity-Based Model of Bounded Rationality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(4), pages 1661-1710.
    6. Ran Spiegler, 2006. "The Market for Quacks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(4), pages 1113-1131.
    7. Kfir Eliaz & Ran Spiegler, 2019. "The Model Selection Curse," American Economic Review: Insights, American Economic Association, vol. 1(2), pages 127-140, September.
    8. Abhijit Banerjee & Sylvain Chassang & Sergio Montero & Erik Snowberg, 2017. "A Theory of Experimenters," NBER Working Papers 23867, National Bureau of Economic Research, Inc.
    9. Mehmet Caner & Kfir Eliaz, 2021. "Shoiuld Humans Lie to Machines: The Incentive Compatibility of Lasso and General Weighted Lasso," Papers 2101.01144, arXiv.org, revised Sep 2021.
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    Cited by:

    1. Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2020. "Manipulation-Proof Machine Learning," Papers 2004.03865, arXiv.org.
    2. Ian Ball, 2019. "Scoring Strategic Agents," Papers 1909.01888, arXiv.org, revised Oct 2023.
    3. Annie Liang & Erik Madsen, 2020. "Data and Incentives," Papers 2006.06543, arXiv.org, revised Sep 2022.

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