arXiv 23 Mar 2024 · Econometrics
arXiv:2403.15934 · PDF · DOI · OpenAlex · Extracted main text
This paper studies debiased machine learning when nuisance parameters appear in indicator functions. An important example is maximized average welfare gain under optimal treatment assignment rules. For asymptotically valid inference for a parameter of interest, the current literature on debiased machine learning relies on Gateaux differentiability of the functions inside moment conditions, which does not hold when nuisance parameters appear in indicator functions. In this paper, we propose smoothing the indicator functions, and develop an asymptotic distribution theory for this class of models. The asymptotic behavior of the proposed estimator exhibits a trade-off between bias and variance due to smoothing. We study how a parameter which controls the degree of smoothing can be chosen optimally to minimize an upper bound of the asymptotic mean squared error. A Monte Carlo simulation supports the asymptotic distribution theory, and an empirical example illustrates the implementation of the method.
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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 | Kitagawa, T. and Tetenov, A (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 1.000 | 10 | 3 | 100% |
| 2 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.874 | 6 | 4 | 67% |
| 3 | Armstrong, T. B. and Kolesár, M (2020) Simple and honest confidence intervals in nonparametric regression | 0.874 | 5 | 2 | 100% |
| 4 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2017) Double/debiased/neyman machine learning of treatment effects | 0.843 | 4 | 3 | 75% |
| 5 | Semenova, V. and Chernozhukov, V (2020) Debiased machine learning of conditional average treatment effects and other causal functions | 0.843 | 3 | 3 | 100% |
| 6 | Chernozhukov, V., Newey, W. K., and Singh, R (2022) Automatic debiased machine learning of causal and structural effects | 0.802 | 33 | 6 | 52% |
| 7 | Levis, A. W., Bonvini, M., Zeng, Z., Keele, L., and Kennedy, E. H (2023) Covariate-assisted bounds on causal effects with instrumental variables | 0.737 | 3 | 2 | 100% |
| 8 | Chernozhukov, V., Escanciano, J. C., Ichimura, H., Newey, W. K., and… (2022) Locally robust semiparametric estimation | 0.693 | 12 | 4 | 33% |
| 9 | Chernozhukov, V., Hansen, C., Kallus, N., Spindler, M., and Syrgkani… (2024) Applied causal inference powered by ml and ai | 0.644 | 2 | 2 | 100% |
| 10 | Hirano, K. and Porter, J. R (2012) Impossibility results for nondifferentiable functionals | 0.644 | 2 | 2 | 100% |
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