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  .
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 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 relies on an easy-to-interpret identifying assumption that mimics the idea of "common trends" in differences-in-differences designs. We use our methods to study the effect of a subsidized loan program on post-education attendance in Colombia , and offer new empirical evidence on the program effects for students with test scores away from the cutoff that determined program eligibility .
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statistical methods for
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critical open question
key area for development
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exploits
relies on
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`common trends'
"common trends"
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illustrate our methods with a study of
use our methods to study
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cash transfer program for
subsidized loan program on
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, and offer new empirical evidence on the program effects for students with test scores away from the cutoff that determined program eligibility