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Shoiuld Humans Lie to Machines: The Incentive Compatibility of Lasso and General Weighted Lasso

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  • Mehmet Caner
  • Kfir Eliaz

Abstract

We consider situations where a user feeds her attributes to a machine learning method that tries to predict her best option based on a random sample of other users. The predictor is incentive-compatible if the user has no incentive to misreport her covariates. Focusing on the popular Lasso estimation technique, we borrow tools from high-dimensional statistics to characterize sufficient conditions that ensure that Lasso is incentive compatible in large samples. We extend our results to the Conservative Lasso estimator and provide new moment bounds for this generalized weighted version of Lasso. Our results show that incentive compatibility is achieved if the tuning parameter is kept above some threshold. We present simulations that illustrate how this can be done in practice.

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  • 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.
    • Handle: RePEc:arx:papers:2101.01144
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    References listed on IDEAS

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    1. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.
    2. Kfir Eliaz & Ran Spiegler, 2019. "The Model Selection Curse," American Economic Review: Insights, American Economic Association, vol. 1(2), pages 127-140, September.
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    4. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    5. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.
    6. Kock, Anders Bredahl, 2016. "Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models," Journal of Econometrics, Elsevier, vol. 195(1), pages 71-85.
    7. Harold D. Chiang, 2018. "Many Average Partial Effects: with An Application to Text Regression," Papers 1812.09397, arXiv.org, revised Jan 2022.
    8. Chiang, Harold D. & Sasaki, Yuya, 2019. "Causal inference by quantile regression kink designs," Journal of Econometrics, Elsevier, vol. 210(2), pages 405-433.
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    1. Eliaz, Kfir & Spiegler, Ran, 2022. "On incentive-compatible estimators," Games and Economic Behavior, Elsevier, vol. 132(C), pages 204-220.

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