Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, Vasilis Syrgkanis
arXiv 6 Oct 2021 · Machine Learning · 5 citations (OpenAlex)
arXiv:2110.03031 · PDF · DOI · OpenAlex · Extracted main text
Many causal and policy effects of interest are defined by linear functionals of high-dimensional or non-parametric regression functions. $\sqrt{n}$-consistent and asymptotically normal estimation of the object of interest requires debiasing to reduce the effects of regularization and/or model selection on the object of interest. Debiasing is typically achieved by adding a correction term to the plug-in estimator of the functional, which leads to properties such as semi-parametric efficiency, double robustness, and Neyman orthogonality. We implement an automatic debiasing procedure based on automatically learning the Riesz representation of the linear functional using Neural Nets and Random Forests. Our method only relies on black-box evaluation oracle access to the linear functional and does not require knowledge of its analytic form. We propose a multitasking Neural Net debiasing method with stochastic gradient descent minimization of a combined Riesz representer and regression loss, while sharing representation layers for the two functions. We also propose a Random Forest method which learns a locally linear representation of the Riesz function. Even though our method applies to arbitrary functionals, we experimentally find that it performs well compared to the state of art neural net based algorithm of Shi et al. (2019) for the case of the average treatment effect functional. We also evaluate our method on the problem of estimating average marginal effects with continuous treatments, using semi-synthetic data of gasoline price changes on gasoline demand.
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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 | Shi, C., Blei, D. M., and Veitch, V (2019) Adapting neural networks for the estimation of treatment effects | 0.969 | 11 | 4 | 91% |
| 2 | Athey, S., Tibshirani, J., and Wager, S (2019) Generalized random forests | 0.874 | 8 | 2 | 100% |
| 3 | Chernozhukov, V., Newey, W. K., Quintas-Martinez, V., and Syrgkanis, V (2021) Automatic debiased machine learning via neural nets for generalized linear regression self | 0.874 | 6 | 2 | 100% |
| 4 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters self | 0.644 | 2 | 2 | 100% |
| 5 | Newey, W. K. and Robins, J. R (2018) Cross-fitting and fast remainder rates for semiparametric estimation self | 0.644 | 2 | 2 | 100% |
| 6 | Rosenbaum, P. R. and Rubin, D. B (1983) The central role of the propensity score in observational studies for causal effects | 0.644 | 2 | 2 | 100% |
| 7 | Athey, S. and Wager, S (2021) Policy learning with observational data | 0.511 | 2 | 1 | 100% |
| 8 | Bang, H. and Robins, J. M (2005) Doubly robust estimation in missing data and causal inference models | 0.511 | 2 | 1 | 100% |
| 9 | Belloni, A., Chen, D., Chernozhukov, V., and Hansen, C (2012) Sparse models and methods for optimal instruments with an application to eminent domain self | 0.511 | 2 | 1 | 100% |
| 10 | Chen, Q., Syrgkanis, V., and Austern, M (2022) Debiased machine learning without sample-splitting for stable estimators self | 0.511 | 2 | 1 | 100% |
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