A Practical Introduction to Regression Discontinuity Designs: Foundations
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Abstract (arXiv)
In this Element and its accompanying Element, Matias D. Cattaneo, Nicolas Idrobo, and Rocio Titiunik provide an accessible and practical guide for the analysis and interpretation of Regression Discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. In this Element, the authors discuss the foundations of the canonical Sharp RD design, which has the following features: (i) the score is continuously distributed and has only one dimension, (ii) there is only one cutoff, and (iii) compliance with the treatment assignment is perfect. In the accompanying Element, the authors discuss practical and conceptual extensions to the basic RD setup.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Nov 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 2; 2025: 1.
6 papers.
- 2024Applied Causal Inference Powered by ML and AIcited by 30
- 2024A Causal Framework for Evaluating Deferring Systemscited by 13
- 2022Quantifying How Hateful Communities Radicalize Online Userscited by 2
- 2021Hierarchical Gaussian Process Models for Regression Discontinuity/Kink under Sharp and Fuzzy Designscited by 0
- 2023Designing Discontinuitiescited by 0
- 2025Effect Identification and Unit Categorization in the Multi-Score Regression Discontinuity Design with Application to LED Manufacturingcited by 0
- 2025Effect Identification and Unit Categorization in the Multi-Score Regression Discontinuity Design with Application to LED Manufacturing21 Aug 2025
- 2024A Causal Framework for Evaluating Deferring Systems29 May 2024
- 2024Applied Causal Inference Powered by ML and AI4 Mar 2024
- 2023Designing Discontinuities15 May 2023
- 2022Quantifying How Hateful Communities Radicalize Online Users19 Sep 2022
- 2021Hierarchical Gaussian Process Models for Regression Discontinuity/Kink under Sharp and Fuzzy Designs3 Oct 2021