arXiv 29 Nov 2023 · Econometrics
arXiv:2311.18136 · PDF · DOI · OpenAlex · Extracted main text
Canonical RD designs yield credible local estimates of the treatment effect at the cutoff under mild continuity assumptions, but they fail to identify treatment effects away from the cutoff without additional assumptions. The fundamental challenge of identifying treatment effects away from the cutoff is that the counterfactual outcome under the alternative treatment status is never observed. This paper aims to provide a methodological blueprint to identify treatment effects away from the cutoff in various empirical settings by offering a non-exhaustive list of assumptions on the counterfactual outcome. Instead of assuming the exact evolution of the counterfactual outcome, this paper bounds its variation using the data and sensitivity parameters. The proposed assumptions are weaker than those introduced previously in the literature, resulting in partially identified treatment effects that are less susceptible to assumption violations. This approach accommodates both single cutoff and multi-cutoff designs. The specific choice of the extrapolation assumption depends on the institutional background of each empirical application. Additionally, researchers are recommended to conduct sensitivity analysis on the chosen parameter and assess resulting shifts in conclusions. The paper compares the proposed identification results with results using previous methods via an empirical application and simulated data. It demonstrates that set identification yields a more credible conclusion about the sign of the treatment effect.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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 | Cattaneo, M. D., Keele, L., Titiunik, R., and Vazquez-Bare, G (2021) Extrapolating treatment effects in multi-cutoff regression discontinuity designs | 1.000 | 16 | 4 | 100% |
| 2 | Angrist, J. D. and Rokkanen, M (2015) Wanna get away? regression discontinuity estimation of exam school effects away from the cutoff | 1.000 | 7 | 4 | 100% |
| 3 | Battistin, E. and Rettore, E (2008) Ineligibles and eligible non-participants as a double comparison group in regression-discontinuity designs | 0.928 | 4 | 3 | 100% |
| 4 | Yata, K (2021) Optimal decision rules under partial identification | 0.843 | 3 | 3 | 100% |
| 5 | Cattaneo, M. D., Keele, L., and Titiunik, R (2023) Covariate adjustment in regression discontinuity designs | 0.737 | 3 | 2 | 100% |
| 6 | Kline, P. and Santos, A (2013) Sensitivity to missing data assumptions: Theory and an evaluation of the us wage structure | 0.737 | 3 | 2 | 100% |
| 7 | Manski, C. F. and Pepper, J. V (2018) How do right-to-carry laws affect crime rates? coping with ambiguity using bounded-variation assumptions | 0.737 | 3 | 2 | 100% |
| 8 | Rambachan, A. and Roth, J (2023) A More Credible Approach to Parallel Trends | 0.737 | 3 | 2 | 100% |
| 9 | Calonico, S., Cattaneo, M. D., Farrell, M. H., and Titiunik, R (2019) Regression discontinuity designs using covariates | 0.644 | 2 | 2 | 100% |
| 10 | Cattaneo, M. D. and Titiunik, R (2022) Regression discontinuity designs | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 41 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs | 0.405 | 1 | 1 |
| 2 | Joint Inference for the Regression Discontinuity Effect and Its External Validity | 0.405 | 1 | 1 |