Dean Eckles, Nikolaos Ignatiadis, Stefan Wager, Han Wu
arXiv 20 Apr 2020 · Statistics — Methodology
arXiv:2004.09458 · PDF · Extracted main text
Regression discontinuity designs assess causal effects in settings where treatment is determined by whether an observed running variable crosses a pre-specified threshold. Here we propose a new approach to identification, estimation, and inference in regression discontinuity designs that uses knowledge about exogenous noise (e.g., measurement error) in the running variable. In our strategy, we weight treated and control units to balance a latent variable of which the running variable is a noisy measure. Our approach is driven by effective randomization provided by the noise in the running variable, and complements standard formal analyses that appeal to continuity arguments while ignoring the stochastic nature of the assignment mechanism.
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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 Imbens and Stefan Wager (2019) Optimized regression discontinuity designs self | 0.956 | 8 | 4 | 88% |
| 2 | David S Lee (2008) Randomized experiments from non-random selection in US House elections | 0.928 | 4 | 3 | 100% |
| 3 | Donald B Rubin (2008) For objective causal inference, design trumps analysis | 0.928 | 4 | 3 | 100% |
| 4 | Timothy B Armstrong and Michal Kolesár (2020) Simple and honest confidence intervals in nonparametric regression | 0.874 | 6 | 2 | 100% |
| 5 | Jacob Bor, Matthew P Fox, Sydney Rosen, Atheendar Venkataramani, Fra… (2017) Treatment eligibility and retention in clinical HIV care: A regression discontinuity study in South Africa | 0.874 | 5 | 2 | 100% |
| 6 | Timothy B Armstrong and Michal Kolesár (2018) Optimal inference in a class of regression models | 0.843 | 4 | 4 | 75% |
| 7 | James J Heckman and Edward Vytlacil (2005) Structural equations, treatment effects, and econometric policy evaluation | 0.843 | 3 | 3 | 100% |
| 8 | Miikka Rokkanen (2015) Exam schools, ability, and the effects of affirmative action: Latent factor extrapolation in the regression discontinuity design | 0.811 | 4 | 2 | 100% |
| 9 | Michal Kolesár and Christoph Rothe (2018) Inference in regression discontinuity designs with a discrete running variable | 0.737 | 3 | 3 | 67% |
| 10 | Sebastian Calonico, Matias D Cattaneo, and Rocío Titiunik (2014) Robust nonparametric confidence intervals for regression-discontinuity designs | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 74 scored citations.
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