Qingliang Fan, Yu-Chin Hsu, Robert P. Lieli, Yichong Zhang
arXiv 6 Aug 2019 · Econometrics · publishedJournal of Business and Economic Statistics (2020) · 93 citations (OpenAlex)
arXiv:1908.02399 · PDF · DOI · OpenAlex · Extracted main text
Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the covariate(s) of interest. We consider two variants of the estimator depending on whether the nuisance functions are estimated over the full sample or over a hold-out sample. Building on Belloni at al. (2017) and Chernozhukov et al. (2018), we derive functional limit theory for the estimators and provide an easy-to-implement procedure for uniform inference based on the multiplier bootstrap. The empirical application revisits the effect of maternal smoking on a baby's birth weight as a function of the mother's age.
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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 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.956 | 8 | 5 | 88% |
| 2 | Belloni, A., V. Chernozhukov, I. Fernández-Val, and C. Hansen (2017) Program evaluation with high-dimensional data | 0.902 | 15 | 7 | 73% |
| 3 | Chernozhukov, V. and V. Semenova (2019) Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions | 0.874 | 6 | 2 | 100% |
| 4 | Farrell, M. H (2015) Robust inference on average treatment effects with possibly more covariates than observations | 0.843 | 3 | 3 | 100% |
| 5 | Lee, S., R. Okui, and Y.-J. Whang (2017) Doubly robust uniform confidence band for the conditional average treatment effect function | 0.843 | 3 | 3 | 100% |
| 6 | Belloni, A., V. Chernozhukov, and C. Hansen (2014) Inference on treatment effects after selection among high-dimensional controls | 0.737 | 3 | 3 | 67% |
| 7 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, a… (2017) Double/debiased/neyman machine learning of treatment effects | 0.737 | 3 | 2 | 100% |
| 8 | Su, L., T. Ura, and Y. Zhang (2019) Non-separable models with high-dimensional data | 0.737 | 3 | 2 | 100% |
| 9 | Chernozhukov, V., D. Chetverikov, and K. Kato (2014) Gaussian approximation of suprema of empirical processes | 0.721 | 8 | 5 | 38% |
| 10 | Kennedy, E. H., Z. Ma, M. D. McHugh, and D. S. Small (2017) Non-parametric methods for doubly robust estimation of continuous treatment effects | 0.644 | 2 | 2 | 100% |
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