arXiv 14 Nov 2021 · Econometrics · publishedJournal of Applied Econometrics (2023) · 14 citations (OpenAlex)
arXiv:2111.07388 · PDF · DOI · OpenAlex · Extracted main text
In many applications of regression discontinuity designs, the running variable used by the administrator to assign treatment is only observed with error. We show that, provided the observed running variable (i) correctly classifies the treatment assignment, and (ii) affects the conditional means of the potential outcomes smoothly, ignoring the measurement error nonetheless yields an estimate with a causal interpretation: the average treatment effect for units whose observed running variable equals to the cutoff. We show that, possibly after doughnut trimming, these assumptions accommodate a variety of settings where support of the measurement error is not too wide. We propose to conduct inference using bias-aware methods, which remain valid even when discreteness or irregular support in the observed running variable may lead to partial identification. We illustrate the results for both sharp and fuzzy designs in an empirical application.
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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 | Armstrong, Timothy B., Kolesár, Michal (2018) Optimal Inference in a Class of Regression Models self | 1.000 | 5 | 3 | 100% |
| 2 | Noack, Claudia, Rothe, Christoph (2021) Bias-Aware Inference in Fuzzy Regression Discontinuity Designs | 0.941 | 6 | 4 | 83% |
| 3 | Armstrong, Timothy B., Kolesár, Michal (2020) Simple and Honest Confidence Intervals in Nonparametric Regression self | 0.874 | 12 | 4 | 67% |
| 4 | Imbens, Guido W., Wager, Stefan (2019) Optimized Regression Discontinuity Designs | 0.822 | 9 | 3 | 56% |
| 5 | Kolesár, Michal, Rothe, Christoph (2018) Inference in Regression Discontinuity Designs with a Discrete Running Variable self | 0.822 | 6 | 2 | 83% |
| 6 | Barreca, Alan I., Lindo, Jason M., Waddell, Glen R (2016) Heaping-Induced Bias in Regression-Discontinuity Designs | 0.737 | 3 | 2 | 100% |
| 7 | Davezies, Laurent, Le Barbanchon, Thomas (2017) Regression Discontinuity Design with Continuous Measurement Error in the Running Variable | 0.737 | 3 | 2 | 100% |
| 8 | Hullegie, Patrick, Klein, Tobias J (2010) The Effect of Private Health Insurance on Medical Care Utilization and Self-Assessed Health in Germany | 0.737 | 3 | 2 | 100% |
| 9 | Pei, Zhuan, Shen, Yi, Cattaneo, Matias D., Escanciano, Juan Carlos (2017) The Devil Is in the Tails: Regression Discontinuity Design with Measurement Error in the Assignment Variable | 0.737 | 3 | 2 | 100% |
| 10 | Battistin, Erich, Brugiavini, Agar, Rettore, Enrico, Weber, Guglielmo (2009) The Retirement Consumption Puzzle: Evidence from a Regression Discontinuity Approach | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 36 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Noise-Induced Randomization in Regression Discontinuity Designs | 0.644 | 2 | 2 |
| 2 | 2009.07551 | 0.405 | 1 | 1 |
| 3 | Joint Inference for the Regression Discontinuity Effect and Its External Validity | 0.405 | 1 | 1 |