arXiv 4 Jan 2021 · Econometrics
arXiv:2101.01144 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Caner, M. and A. B. Kock (2018) Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative lasso self | 0.852 | 21 | 6 | 62% |
| 2 | Jankova, J. and S. van de Geer (2018) Semi-parametric efficiency bounds for high-dimensional models | 0.814 | 13 | 6 | 54% |
| 3 | Chernozhukov, V., D. Chetverikov, and K. Kato (2017) Central limit theorems and bootstrap in high dimensions | 0.511 | 3 | 2 | 33% |
| 4 | Buhlmann, P. and S. van de Geer (2011) Statitistics for High-Dimensional Data | 0.511 | 2 | 2 | 50% |
| 5 | van de Geer, S (2016) Estimation and testing under sparsity | 0.511 | 2 | 2 | 50% |
| 6 | Eliaz, K. and R. Spiegler (2019) The model selection curse self | 0.511 | 2 | 1 | 100% |
| 7 | Eliaz, K. and R. Spiegler (2020) On incentive compatible estimators self | 0.511 | 2 | 1 | 100% |
| 8 | van de Geer, S., P. Bühlmann, Y. Ritov, and R. Dezeure (2014) On asymptotically optimal confidence regions and tests for high-dimensional models | 0.511 | 2 | 1 | 100% |
| 9 | Chiang, H (2020) Many average partial effects: with an application to text regression | 0.405 | 1 | 1 | 100% |
| 10 | Cai, Y., C. Daskalakis, and C. Papadimitrou (2015) Optimum statistical estimation with strategic data sources | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 27 scored citations.