Matias D. Cattaneo, Rocio Titiunik
arXiv 20 Aug 2021 · Econometrics · publishedAnnual Review of Economics (2022) · 33 citations (OpenAlex)
arXiv:2108.09400 · PDF · DOI · OpenAlex · Extracted main text
The Regression Discontinuity (RD) design is one of the most widely used non-experimental methods for causal inference and program evaluation. Over the last two decades, statistical and econometric methods for RD analysis have expanded and matured, and there is now a large number of methodological results for RD identification, estimation, inference, and validation. We offer a curated review of this methodological literature organized around the two most popular frameworks for the analysis and interpretation of RD designs: the continuity framework and the local randomization framework. For each framework, we discuss three main topics: (i) designs and parameters, which focuses on different types of RD settings and treatment effects of interest; (ii) estimation and inference, which presents the most popular methods based on local polynomial regression and analysis of experiments, as well as refinements, extensions, and alternatives; and (iii) validation and falsification, which summarizes an array of mostly empirical approaches to support the validity of RD designs in practice.
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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 | Cattaneo, Titiunik, and Vazquez-Bare (2017) Comparing Inference Approaches for RD Designs: A Reexamination of the Effect of Head Start on Child Mortality | 1.000 | 6 | 4 | 100% |
| 2 | Cattaneo, Idrobo, and Titiunik (2022) A Practical Introduction to Regression Discontinuity Designs: Extensions self | 1.000 | 5 | 3 | 100% |
| 3 | Cattaneo, Idrobo, and Titiunik (2020) A Practical Introduction to Regression Discontinuity Designs: Foundations self | 0.928 | 4 | 3 | 100% |
| 4 | Calonico, Cattaneo, and Titiunik (2014) Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs self | 0.874 | 6 | 2 | 100% |
| 5 | Cattaneo, Keele, Titiunik, and Vazquez-Bare (2016) Interpreting Regression Discontinuity Designs with Multiple Cutoffs | 0.874 | 6 | 2 | 100% |
| 6 | Thistlethwaite and Campbell (1960) Regression-Discontinuity Analysis: An Alternative to the Ex-Post Facto Experiment | 0.843 | 3 | 3 | 100% |
| 7 | Cattaneo, Frandsen, and Titiunik (2015) Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate self | 0.811 | 4 | 2 | 100% |
| 8 | Cattaneo, Keele, Titiunik, and Vazquez-Bare (2021) Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs | 0.811 | 4 | 2 | 100% |
| 9 | Keele and Titiunik (2015) Geographic Boundaries as Regression Discontinuities | 0.811 | 4 | 2 | 100% |
| 10 | De Magalhaes, Hangartner, Hirvonen, Meriläinen, Ruiz, and Tukiainen (2020) How Much Should We Trust Regression Discontinuity Design Estimates? Evidence from Experimental Benchmarks of the Incumbency Adva… | 0.737 | 3 | 2 | 100% |
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