Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs

Matias D. Cattaneo, Luke Keele, Rocío Titiunik, Gonzalo Vazquez-Bare

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Abstract–In nonexperimental settings, the regression discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference. However, RD treatment effect estimands are necessarily local, making statistical methods for the extrapolation of these effects a key area for development. We introduce a new method for extrapolation of RD effects that relies on the presence of multiple cutoffs, and is therefore design-based. Our approach employs an easy-to-interpret identifying assumption that mimics the idea of “common trends” in difference-in-differences designs. We illustrate our methods with data on a subsidized loan program on post-education attendance in Colombia, and offer new evidence on program effects for students with test scores away from the cutoff that determined program eligibility. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1941-1952
Number of pages12
JournalJournal of the American Statistical Association
Issue number536
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Causal inference
  • Extrapolation
  • Regression discontinuity


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