# Regression Discontinuity Designs

arXiv: https://arxiv.org/abs/2108.09400 · HTML: https://mlsanity.com/paper/2108.09400/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2021.

## Built on by (most cited first, 5 of 5)

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- [Computational Experiments Meet Large Language Model Based Agents: A Survey and Perspective](https://mlsanity.com/paper/2402.00262/) (2024)
- [Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm](https://mlsanity.com/paper/2409.04500/) (2024)
- [Hierarchical Gaussian Process Models for Regression Discontinuity/Kink under Sharp and Fuzzy Designs](https://mlsanity.com/paper/2110.00921/) (2021)
- [Forests for Differences: Robust Causal Inference Beyond Parametric DiD](https://mlsanity.com/paper/2505.09706/) (2025)

## Abstract (arXiv)

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.