Liang Jiang, Oliver B. Linton, Haihan Tang, Yichong Zhang
arXiv 31 Jan 2022 · Econometrics · publishedThe Review of Economics and Statistics (2024) · 5 citations (OpenAlex)
arXiv:2201.13004 · PDF · DOI · OpenAlex · Extracted main text
We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-adjusted estimators, which are based on the doubly robust moment for local average treatment effects, are consistent and asymptotically normal even with heterogeneous probability of assignment and misspecified regression adjustments. We propose an optimal but potentially misspecified linear adjustment and its further improvement via a nonlinear adjustment, both of which lead to more efficient estimators than the one without adjustments. We also provide conditions for nonparametric and regularized adjustments to achieve the semiparametric efficiency bound under CARs.
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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 | Bugni, F. A. and M. Gao (2021) Inference under covariate-adaptive randomization with imperfect compliance | 1.000 | 11 | 4 | 100% |
| 2 | Frölich, M (2007) Nonparametric iv estimation of local average treatment effects with covariates | 0.941 | 6 | 4 | 83% |
| 3 | Bugni, F. A., I. A. Canay, and A. M. Shaikh (2018) Inference under covariate-adaptive randomization | 0.941 | 6 | 3 | 83% |
| 4 | Dupas, P., D. Karlan, J. Robinson, and D. Ubfal (2018) Banking the unbanked? evidence from three countries | 0.874 | 9 | 2 | 100% |
| 5 | Armstrong, T. B (2022) Asymptotic efficiency bounds for a class of experimental designs | 0.874 | 6 | 3 | 67% |
| 6 | Ansel, J., H. Hong, and J. Li (2018) Ols and 2sls in randomised and conditionally randomized experiments | 0.867 | 23 | 7 | 65% |
| 7 | Belloni, A., V. Chernozhukov, I. Fernández-Val, and C. Hansen (2017) Program evaluation with high-dimensional data | 0.794 | 6 | 4 | 50% |
| 8 | Bugni, F. A., I. A. Canay, and A. M. Shaikh (2019) Inference under covariate-adaptive randomization with multiple treatments | 0.644 | 2 | 2 | 100% |
| 9 | Zhang, Y. and X. Zheng (2020) Quantile treatment effects and bootstrap inference under covariate-adaptive randomization self | 0.644 | 2 | 2 | 100% |
| 10 | Hirano, K., G. W. Imbens, and G. Ridder (2003) Efficient estimation of average treatment effects using the estimated propensity score | 0.585 | 3 | 3 | 33% |
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