arXiv 21 Nov 2019 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:1911.09248 · PDF · DOI · OpenAlex · Extracted main text
In Regression Discontinuity (RD) design, self-selection leads to different distributions of covariates on two sides of the policy intervention, which essentially violates the continuity of potential outcome assumption. The standard RD estimand becomes difficult to interpret due to the existence of some indirect effect, i.e. the effect due to self selection. We show that the direct causal effect of interest can still be recovered under a class of estimands. Specifically, we consider a class of weighted average treatment effects tailored for potentially different target populations. We show that a special case of our estimands can recover the average treatment effect under the conditional independence assumption per Angrist and Rokkanen (2015), and another example is the estimand recently proposed in Fr\"olich and Huber (2018). We propose a set of estimators through a weighted local linear regression framework and prove the consistency and asymptotic normality of the estimators. Our approach can be further extended to the fuzzy RD case. In simulation exercises, we compare the performance of our estimator with the standard RD estimator. Finally, we apply our method to two empirical data sets: the U.S. House elections data in Lee (2008) and a novel data set from Microsoft Bing on Generalized Second Price (GSP) auction.
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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 | Lee, D. S (2008) Randomized experiments from non-random selection in u.s. house elections | 1.000 | 10 | 5 | 100% |
| 2 | Frölich, M. and Huber, M (2018) Including covariates in the regression discontinuity design | 1.000 | 6 | 3 | 100% |
| 3 | Angrist, J. and Rokkanen, M (2015) Wanna get away? regression discontinuity estimation of exam school effects away from the cutoff | 0.928 | 4 | 3 | 100% |
| 4 | Hahn, J., Todd, P. and der Klaauw, W. V (2001) Identification and estimation of treatment effects with a regression-discontinuity design | 0.928 | 4 | 3 | 100% |
| 5 | Calonicoy, S., Cattaneo, M., Farrellx, M. and Titiunik, R (2017) Regression discontinuity designs using covariates | 0.928 | 4 | 3 | 100% |
| 6 | Cattaneo, M. D., Frandsen, B. R. and Titiunik, R (2015) Randomization inference in the regression discontinuity design: An application to party advantages in the us senate | 0.737 | 3 | 2 | 100% |
| 7 | Horvitz, D. G. and Thompson, D. J (1952) A generalization of sampling without replacement from a finite universe | 0.644 | 2 | 2 | 100% |
| 8 | Imbens, G. W. and Lemieux, T (2008) Regression discontinuity designs: A guide to practice | 0.644 | 2 | 2 | 100% |
| 9 | Abadie, A. and Imbens, G. W (2016) Matching on the estimated propensity score | 0.405 | 1 | 1 | 100% |
| 10 | Canay, I. A. and Kamat, V (2018) Approximate permutation tests and induced order statistics in the regression discontinuity design | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 17 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Regression Discontinuity Designs | 0.405 | 1 | 1 |
| 2 | Covariate Adjustment in Regression Discontinuity Designs | 0.405 | 1 | 1 |