论文标题
当测试分数是运行变量时，本地随机回归不连续性设计
Local Randomization Regression Discontinuity Designs when Test Scores are the Running Variable
论文作者
论文摘要
回归不连续设计（RDD）内部有效性（RDD）的解释通常吸引了这样一种想法：RDD与“在治疗切口附近随机”一样好。Cattaneo，Frandsen和Titiunik（2015）是第一个将这种理由以全面的结论进行理由，并提出了与RDD的平均治疗效应（与RDD的平均效果相同的范围），这与RDD相同的效果（将其与RDD相同）相同（一项分析），这是一项分析的实验。 RDD作为局部随机实验，当运行变量是一种测试分数。当运行变量是测试得分或其他人类开发的度量（例如，医学测试）时，估算RDD的方法是有问题的。
Explanations of the internal validity of regression discontinuity designs (RDD) generally appeal to the idea that RDDs are ``as good as" random near the treatment cut point. Cattaneo, Frandsen, and Titiunik (2015) are the first to take this justification to its full conclusion and propose estimating the RDD local average treatment effect (LATE) the same as one would a randomized experiment. This paper explores the implications of analyzing an RDD as a local random experiment when the running variable is a test score. I derive a formula for the bias in the LATE estimate estimated using the local randomization method, $aρΔ$. Where $a$ is the relationship between latent proficiency and the potential outcome absent treatment, $ρ$ is the test reliability, and $Δ$ is the distance between the treatment and control running variable value. I use this quantification of the bias to demonstrate that local randomization and related design based methods for estimating RDDs are problematic when the running variable is test score or other human developed measure (e.g., medical tests).
