arXiv 22 Jun 2021 · Econometrics · 3 citations (OpenAlex)
arXiv:2106.11640 · PDF · DOI · OpenAlex · Extracted main text
The paper proposes a causal supervised machine learning algorithm to uncover treatment effect heterogeneity in sharp and fuzzy regression discontinuity (RD) designs. We develop a criterion for building an honest “regression discontinuity tree”, where each leaf contains the RD estimate of a treatment conditional on the values of some pre-treatment covariates. It is a priori unknown which covariates are relevant for capturing treatment effect heterogeneity, and it is the task of the algorithm to discover them, without invalidating inference, while employing a nonparametric estimator with expected MSE optimal bandwidth. We study the performance of the method through Monte Carlo simulations and apply it to uncover various sources of heterogeneity in the impact of attending a better secondary school in Romania.
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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 | Pop-Eleches \ Urquiola (2013) `Going to a better school: Effects and behavioral responses', American Economic Review 103(4), 1289–1324 | 1.000 | 15 | 3 | 100% |
| 2 | Wager \ Athey (2018) `Estimation and inference of heterogeneous treatment effects using random forests', Journal of the American Statistical Associat… | 1.000 | 8 | 3 | 100% |
| 3 | Calonico, Cattaneo \ Titiunik (2014) `Robust nonparametric confidence intervals for regression-discontinuity designs', Econometrica 82(6), 2295–2326 | 1.000 | 7 | 3 | 100% |
| 4 | Hsu \ Shen (2019) `Testing treatment effect heterogeneity in regression discontinuity designs', Journal of Econometrics 208(2), 468–486 | 1.000 | 5 | 3 | 100% |
| 5 | Athey \ Imbens (2016) `Recursive partitioning for heterogeneous causal effects', Proceedings of the National Academy of Sciences 113(27), 7353–7360 | 0.874 | 10 | 2 | 100% |
| 6 | Lee \ Lemieux (2010) `Regression discontinuity designs in economics', Journal of Economic Literature 48(2), 281–355 | 0.874 | 5 | 2 | 100% |
| 7 | Imai, Ratkovic et al (2013) `Estimating treatment effect heterogeneity in randomized program evaluation', The Annals of Applied Statistics 7(1), 443–470 | 0.737 | 3 | 2 | 100% |
| 8 | Calonico, Cattaneo \ Farrell (2020) `Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs', The Econometrics Journal 23,… | 0.693 | 5 | 1 | 100% |
| 9 | Cattaneo \ Titiunik (2022) `Regression discontinuity designs', Annual Review of Economics 14(1), 821–851 | 0.644 | 2 | 2 | 100% |
| 10 | Imbens \ Lemieux (2008) `Regression discontinuity designs: A guide to practice', Journal of Econometrics 142(2), 615–635 | 0.644 | 2 | 2 | 100% |
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