Zhexiao Lin, Fang Han
arXiv 11 Dec 2022 · Mathematics — Statistics Theory · publishedJournal of Econometrics (2025)
arXiv:2212.05424 · PDF · DOI · OpenAlex · Extracted main text
Imputing missing potential outcomes using an estimated regression function is a natural idea for estimating causal effects. In the literature, estimators that combine imputation and regression adjustments are believed to be comparable to augmented inverse probability weighting. Accordingly, people for a long time conjectured that such estimators, while avoiding directly constructing the weights, are also doubly robust (Imbens, 2004; Stuart, 2010). Generalizing an earlier result of the authors (Lin et al., 2021), this paper formalizes this conjecture, showing that a large class of regression-adjusted imputation methods are indeed doubly robust for estimating the average treatment effect. In addition, they are provably semiparametrically efficient as long as both the density and regression models are correctly specified. Notable examples of imputation methods covered by our theory include kernel matching, (weighted) nearest neighbor matching, local linear matching, and (honest) random forests.
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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 | Wager, S. and Athey, S (2018) Estimation and inference of heterogeneous treatment effects using random forests | 1.000 | 13 | 4 | 100% |
| 2 | Abadie, A. and Imbens, G. W (2011) Bias-corrected matching estimators for average treatment effects | 1.000 | 11 | 4 | 100% |
| 3 | Heckman, J. J., Ichimura, H., and Todd, P (1998) Matching as an econometric evaluation estimator | 1.000 | 6 | 3 | 100% |
| 4 | Abadie, A. and Imbens, G. W (2006) Large sample properties of matching estimators for average treatment effects | 0.956 | 8 | 4 | 88% |
| 5 | Heckman, J. J., Ichimura, H., and Todd, P. E (1997) Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme | 0.928 | 4 | 3 | 100% |
| 6 | Heckman, J. J., Ichimura, H., Smith, J. A., and Todd, P. E (1998) Characterizing selection bias using experimental data | 0.928 | 4 | 3 | 100% |
| 7 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.855 | 8 | 3 | 62% |
| 8 | Athey, S. and Imbens, G (2016) Recursive partitioning for heterogeneous causal effects | 0.843 | 3 | 3 | 100% |
| 9 | Fan, J. and Gijbels, I (1996) Local Polynomial Modelling and its Applications | 0.843 | 3 | 3 | 100% |
| 10 | Stuart, E. A (2010) Matching methods for causal inference: A review and a look forward | 0.843 | 3 | 3 | 100% |
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