Michael Zimmert, Michael Lechner
arXiv 23 Aug 2019 · Econometrics · 26 citations (OpenAlex)
arXiv:1908.08779 · PDF · DOI · OpenAlex · Extracted main text
This paper considers the practically important case of nonparametrically estimating heterogeneous average treatment effects that vary with a limited number of discrete and continuous covariates in a selection-on-observables framework where the number of possible confounders is very large. We propose a two-step estimator for which the first step is estimated by machine learning. We show that this estimator has desirable statistical properties like consistency, asymptotic normality and rate double robustness. In particular, we derive the coupled convergence conditions between the nonparametric and the machine learning steps. We also show that estimating population average treatment effects by averaging the estimated heterogeneous effects is semi-parametrically efficient. The new estimator is an empirical example of the effects of mothers' smoking during pregnancy on the resulting birth weight.
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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 | Hahn, Jinyong (1998) On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects | 1.000 | 10 | 3 | 100% |
| 2 | Abrevaya, Jason, Hsu, Yu-Chin, Lieli, Robert P (2015) Estimating Conditional Average Treatment Effects | 1.000 | 9 | 3 | 100% |
| 3 | Chernozhukov, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esth… (2018) Double/Debiased Machine Learning for Treatment and Structural Parameters | 1.000 | 8 | 4 | 100% |
| 4 | Lee, Sokbae, Okui, Ryo, Whang, Yoon-Jae (2016) Doubly robust uniform confidence band for the conditional average treatment effect function | 0.969 | 11 | 4 | 91% |
| 5 | Newey, Whitney K (1994) The Asymptotic Variance of Semiparametric Estimators | 0.843 | 10 | 3 | 60% |
| 6 | Hirano, Keisuke, Imbens, Guido W., Ridder, Geert (2003) Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score | 0.811 | 4 | 2 | 100% |
| 7 | Pagan, Adrian, Ullah, Aman (1999) Nonparametric Econometrics | 0.644 | 4 | 2 | 50% |
| 8 | Hahn, Jinyong, Ridder, Geert (2013) Asymptotic Variance of Semiparametric Estimators With Generated Regressors | 0.644 | 2 | 2 | 100% |
| 9 | Kennedy, Edward H., Ma, Zongming, McHugh, Matthew D., Small, Dylan S (2017) Non-Parametric Methods for Doubly Robust Estimation of Continuous Treatment Effects | 0.644 | 2 | 2 | 100% |
| 10 | Lechner, Michael (2018) Modified Causal Forests for Estimating Heterogeneous Causal Effects self | 0.585 | 3 | 1 | 100% |
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