Approaches to the Estimation of the Local Average Treatment Effect in a Regression Discontinuity Design
本文比较了三种估计断点回归设计中局部平均处理效应的方法：两阶段最小二乘法、似然法和贝叶斯方法，并通过模拟和真实案例（基于心血管疾病风险评分的他汀类药物处方）进行对比。
Regression discontinuity designs (RD designs) are used as a method for causal inference from observational data, where the decision to apply an intervention is made according to a 'decision rule' that is linked to some continuous variable. Such designs are being increasingly developed in medicine. The local average treatment effect (LATE) has been established as an estimator of the intervention effect in an RD design, particularly where a design's 'decision rule' is not adhered to strictly. Estimating the variance of the LATE is not necessarily straightforward. We consider three approaches to the estimation of the LATE: two-stage least squares, likelihood-based and a Bayesian approach. We compare these under a variety of simulated RD designs and a real example concerning the prescription of statins based on cardiovascular disease risk score.