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= = 实例 = = 通过对基于成绩的奖学金的评估，可以很好地说明 RDD 背后的直觉。估计这种干预的因果效应的主要问题是对待遇分配的表现的同质性(例如:。奖学金)。由于表现优异的学生更有可能获得优异奖学金，同时继续表现良好，因此比较获奖学生和非获奖学生的成绩，估计数字会偏向上方。即使奖学金根本没有提高成绩，获奖者的成绩也会比没有获奖者的成绩要好，这仅仅是因为奖学金是颁发给事先表现良好的学生。

= = 实例 = = 通过对基于成绩的奖学金的评估，可以很好地说明 RDD 背后的直觉。估计这种干预的因果效应的主要问题是对待遇分配的表现的同质性(例如:。奖学金)。由于表现优异的学生更有可能获得优异奖学金，同时继续表现良好，因此比较获奖学生和非获奖学生的成绩，估计数字会偏向上方。即使奖学金根本没有提高成绩，获奖者的成绩也会比没有获奖者的成绩要好，这仅仅是因为奖学金是颁发给事先表现良好的学生。

Despite the absence of an [[experimental design]], an RDD can exploit [[exogenous]] characteristics of the intervention to elicit [[causality|causal effects]]. If all students above a given grade — for example 80% — are given the scholarship, it is possible to elicit the local treatment effect by comparing students around the 80% cut-off. The intuition here is that a student scoring 79% is likely to be very similar to a student scoring 81% — given the pre-defined threshold of 80%. However, one student will receive the scholarship while the other will not. Comparing the outcome of the awardee (treatment group) to the [[Counterfactual conditional|counterfactual]] outcome of the non-recipient (control group) will hence deliver the local treatment effect.

Despite the absence of an [[experimental design]], an RDD can exploit [[exogenous]] characteristics of the intervention to elicit [[causality|causal effects]]. If all students above a given grade — for example 80% — are given the scholarship, it is possible to elicit the local treatment effect by comparing students around the 80% cut-off. The intuition here is that a student scoring 79% is likely to be very similar to a student scoring 81% — given the pre-defined threshold of 80%. However, one student will receive the scholarship while the other will not. Comparing the outcome of the awardee (treatment group) to the [[Counterfactual conditional|counterfactual]] outcome of the non-recipient (control group) will hence deliver the local treatment effect.