Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs

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.

Automated summary

The regression discontinuity (RD) design is a credible identification strategy for program evaluation and causal inference in nonexperimental settings. However, RD treatment effect estimands are local, so developing statistical methods for extrapolation of these effects is key. A new design-based method relying on the presence of multiple cutoffs is introduced, with an easy-to-interpret identifying assumption mimicking common trends. Illustration with data on a subsidized loan program in Colombia offers new evidence on program effects for students away from the eligibility cutoff.