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.

摘要-在非实验环境中，回归不连续性 (RD) 设计是用于程序评估和因果推断的最可靠的识别策略之一。然而，RD 治疗效果估计值必然是局部的，这使得推断这些效果的统计方法成为发展的关键领域。我们引入了一种新的 RD 效应外推方法，该方法依赖于多个临界值的存在，因此是基于设计的。我们的方法采用了一个易于解释的识别假设，该假设模仿了差异中差异设计中“共同趋势”的想法。我们用关于哥伦比亚毕业后出勤补贴贷款计划的数据来说明我们的方法，并为那些考试成绩偏离确定计划资格的截止点的学生提供计划影响的新证据。