Regression Discontinuity Designs

Software packages for analysis and interpretation of regression discontinuity designs and related methods. Replication files and illustration codes employing these packages are also available.

This work was supported in part by the National Science Foundation through grants SES-1357561, SES-1459931, SES-1459967, SES-1947662, SES-1947805, SES-2019432, and SES-2241575, and by the National Institutes of Health through grant R01 GM072611-16.

Queries and Requests

Please email: rdpackages@googlegroups.com

Software available in Python, R and Stata

• rdrobust: estimation, inference and graphical procedures using local polynomial and partitioning regression methods.
• rdhte: estimation and inference for heterogeneous treatment effects.
• rdlocrand: finite-sample and large-sample estimation and inference using local randomization and related methods.
• rddensity: manipulation testing using local polynomial density methods.
• rdpower: power, sample size, and minimum detectable effects calculations using robust bias-corrected local polynomial inference.
• rd2d: estimation and inference for boundary discontinuity designs.
• rdmulti: estimation, inference, RD Plots, and extrapolation with multiple cutoffs and multiple scores.

Replication

Replication files and illustration code are available in the replication page.

Contributors

• Sebastian Calonico, UC Davis.
• Matias D. Cattaneo, Princeton University.
• Rajita Chandak, University of Wisconsin-Madison.
• Nicolas Idrobo, University of Pennsylvania.
• Max H. Farrell, UC Santa Barbara.
• Michael Jansson, UC Berkeley.
• Xinwei Ma, UC San Diego.
• Ricardo Masini, UC Davis.
• Filippo Palomba, Princeton University.
• Rocio Titiunik, Princeton University.
• Gonzalo Vazquez-Bare, UC Santa Barbara.
• Ruiqi (Rae) Yu, Princeton University.

Reviews and Introductions

• Cattaneo and Titiunik (2022): Regression Discontinuity Designs.
  Annual Review of Economics 14: 821-851.

• Cattaneo, Idrobo and Titiunik (2020): A Practical Introduction to Regression Discontinuity Designs: Foundations.
  Cambridge Elements: Quantitative and Computational Methods for Social Science, Cambridge University Press. Erratum.

• Cattaneo, Idrobo and Titiunik (2024): A Practical Introduction to Regression Discontinuity Designs: Extensions.
  Cambridge Elements: Quantitative and Computational Methods for Social Science, Cambridge University Press.

• Cattaneo, Keele and Titiunik (2023): A Guide to Regression Discontinuity Designs in Medical Applications.
  Statistics in Medicine 42(24): 4484-4513.

• Cattaneo, Titiunik and Vazquez-Bare (2020): The Regression Discontinuity Design.
  Handbook of Research Methods in Political Science and International Relations, Sage Publications, Ch. 44, pp. 835-857.

• Cattaneo, Titiunik and Vazquez-Bare (2017): Comparing Inference Approaches for RD Designs: A Reexamination of the Effect of Head Start on Child Mortality.
  Journal of Policy Analysis and Management 36(3): 643-681.

• Cattaneo and Titiunik (2024): Comment: Protocols for Observational Studies: An Application to Regression Discontinuity Designs.
  Statistical Science 39(4): 560-565.

• Cattaneo and Vazquez-Bare (2016): The Choice of Neighborhood in Regression Discontinuity Designs.
  Observational Studies 2: 134-146.