arXiv 21 Mar 2023 · Econometrics
arXiv:2303.11721 · PDF · DOI · OpenAlex · Extracted main text
We discuss estimation and inference of conditional treatment effects in regression discontinuity (RD) designs with multiple scores. In addition to local linear regressions and the minimax-optimal estimator more recently proposed by Imbens and Wager (2019), we argue that two variants of random forests, honest regression forests and local linear forests, should be added to the toolkit of applied researchers working with multivariate RD designs; their validity follows from results in Wager and Athey (2018) and Friedberg et al. (2020). We design a systematic Monte Carlo study with data generating processes built both from functional forms that we specify and from Wasserstein Generative Adversarial Networks that closely mimic the observed data. We find no single estimator dominates across all specifications: (i) local linear regressions perform well in univariate settings, but the common practice of reducing multivariate scores to a univariate one can incur under-coverage, possibly due to vanishing density at the transformed cutoff; (ii) good performance of the minimax-optimal estimator depends on accurate estimation of a nuisance parameter and its current implementation only accepts up to two scores; (iii) forest-based estimators are not designed for estimation at boundary points and are susceptible to finite-sample bias, but their flexibility in modeling multivariate scores opens the door to a wide range of empirical applications, as illustrated by an empirical study of COVID-19 hospital funding with three eligibility criteria.
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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 S. Athey (2018) Estimation and inference of heterogeneous treatment effects using random forests | 1.000 | 11 | 3 | 100% |
| 2 | Zajonc, T (2012) Regression discontinuity design with multiple forcing variables | 1.000 | 5 | 3 | 100% |
| 3 | Friedberg, R., J. Tibshirani, S. Athey, and S. Wager (2020) Local linear forests | 0.979 | 16 | 4 | 94% |
| 4 | Imbens, G. and S. Wager (2019) Optimized regression discontinuity designs | 0.974 | 13 | 5 | 92% |
| 5 | Calonico, S., M. D. Cattaneo, and R. Titiunik (2014) Robust nonparametric confidence intervals for regression-discontinuity designs | 0.950 | 7 | 4 | 86% |
| 6 | Imbens, G. and K. Kalyanaraman (2012) Optimal bandwidth choice for the regression discontinuity estimator | 0.928 | 5 | 4 | 80% |
| 7 | Athey, S., J. Tibshirani, and S. Wager (2019) Generalized random forests | 0.928 | 5 | 3 | 80% |
| 8 | Keele, L. J. and R. Titiunik (2015) Geographic boundaries as regression discontinuities | 0.909 | 16 | 5 | 75% |
| 9 | Lee, D. S (2008) Randomized experiments from non-random selection in us house elections | 0.874 | 9 | 3 | 67% |
| 10 | Narita, Y. and K. Yata (2023) Algorithm is experiment: Machine learning, market design, and policy eligibility rules | 0.874 | 8 | 2 | 100% |
Showing the top 10 of 35 scored citations.