arXiv 12 Nov 2024 · Econometrics
arXiv:2411.07978 · PDF · DOI · OpenAlex · Extracted main text
This note introduces a doubly robust (DR) estimator for regression discontinuity (RD) designs. RD designs provide a quasi-experimental framework for estimating treatment effects, where treatment assignment depends on whether a running variable surpasses a predefined cutoff. A common approach in RD estimation is the use of nonparametric regression methods, such as local linear regression. However, the validity of these methods still relies on the consistency of the nonparametric estimators. In this study, we propose the DR-RD estimator, which combines two distinct estimators for the conditional expected outcomes. The primary advantage of the DR-RD estimator lies in its ability to ensure the consistency of the treatment effect estimation as long as at least one of the two estimators is consistent. Consequently, our DR-RD estimator enhances robustness of treatment effect estimators in RD designs.
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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 | Guido W. Imbens and Thomas Lemieux (2008) Regression discontinuity designs: A guide to practice | 0.511 | 2 | 1 | 100% |
| 2 | Joshua D. Angrist and Victor Lavy (1999) Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement* | 0.405 | 1 | 1 | 100% |
| 3 | Heejung Bang and James M. Robins (2005) Doubly robust estimation in missing data and causal inference models | 0.405 | 1 | 1 | 100% |
| 4 | Sandra E. Black (1999) Do Better Schools Matter? Parental Valuation of Elementary Education* | 0.405 | 1 | 1 | 100% |
| 5 | Masaaki Imaizumi and Kenji Fukumizu (2019) Deep neural networks learn non-smooth functions effectively | 0.405 | 1 | 1 | 100% |
| 6 | Guido W. Imbens and Donald B. Rubin (2015) Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction | 0.405 | 1 | 1 | 100% |
| 7 | Guido W. Imbens and Jeffrey M. Wooldridge (2009) Recent developments in the econometrics of program evaluation | 0.405 | 1 | 1 | 100% |
| 8 | Wilbert Van Der Klaauw (2002) Estimating the effect of financial aid offers on college enrollment: A regression–discontinuity approach | 0.405 | 1 | 1 | 100% |
| 9 | David S. Lee (2008) Randomized experiments from non-random selection in u.s. house elections | 0.405 | 1 | 1 | 100% |
| 10 | Jerzy Neyman (1923) Sur les applications de la theorie des probabilites aux experiences agricoles: Essai des principes | 0.405 | 1 | 1 | 100% |
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