Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs

08/13/2018

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by   Matias D. Cattaneo, et al.

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Regression discontinuity (RD) designs are viewed as one of the most credible identification strategies for program evaluation and causal inference. However, RD treatment effect estimands are necessarily local, making the extrapolation of these effects a critical open question. We introduce a new method for extrapolation of RD effects that exploits the presence of multiple cutoffs, and is therefore design-based. Our approach relies on an easy-to-interpret identifying assumption that mimics the idea of `common trends' in differences-in-differences designs. We illustrate our methods with a study of the effect of a cash transfer program for post-education attendance in Colombia.