Claudia Noack, Tomasz Olma, Christoph Rothe
arXiv 16 Jul 2021 · Econometrics · 3 citations (OpenAlex)
arXiv:2107.07942 · PDF · DOI · OpenAlex · Extracted main text
Empirical regression discontinuity (RD) studies often include covariates in their specifications to increase the precision of their estimates. In this paper, we propose a novel class of estimators that use such covariate information more efficiently than existing methods and can accommodate many covariates. Our estimators are simple to implement and involve running a standard RD analysis after subtracting a function of the covariates from the original outcome variable. We characterize the function of the covariates that minimizes the asymptotic variance of these estimators. We also show that the conventional RD framework gives rise to a special robustness property which implies that the optimal adjustment function can be estimated flexibly via modern machine learning techniques without affecting the first-order properties of the final RD estimator. We demonstrate our methods' scope for efficiency improvements by reanalyzing data from a large number of recently published empirical studies.
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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 | Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik (2019) Regression Discontinuity Designs Using Covariates | 1.000 | 9 | 4 | 100% |
| 2 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.971 | 12 | 5 | 92% |
| 3 | Hahn, J., P. Todd, and W. Van der Klaauw (2001) Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design | 0.928 | 4 | 3 | 100% |
| 4 | Calonico, S., M. D. Cattaneo, and R. Titiunik (2014) Robust nonparametric confidence intervals for regression-discontinuity designs | 0.909 | 8 | 5 | 75% |
| 5 | Armstrong, T. B. and M. Kolesár (2020) Simple and honest confidence intervals in nonparametric regression | 0.874 | 6 | 5 | 67% |
| 6 | Imbens, G. and S. Wager (2019) Optimized regression discontinuity designs | 0.874 | 6 | 4 | 67% |
| 7 | Imbens, G. and K. Kalyanaraman (2012) Optimal bandwidth choice for the regression discontinuity estimator | 0.843 | 3 | 3 | 100% |
| 8 | Wager, S., W. Du, J. Taylor, and R. J. Tibshirani (2016) High-dimensional regression adjustments in randomized experiments | 0.811 | 4 | 2 | 100% |
| 9 | Krei, A. and C. Rothe (2023) Inference in regression discontinuity designs with high-dimensional covariates | 0.737 | 3 | 2 | 100% |
| 10 | Belloni, A., V. Chernozhukov, I. Fernández-Val, and C. Hansen (2017) Program Evaluation and Causal Inference With High-Dimensional Data | 0.644 | 2 | 2 | 100% |
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