Steiner Peter M, Kim Yongnam, Hall Courtney E, Su Dan
Department of Educational Psychology, University of Wisconsin-Madison, Madison, WI, USA.
Sociol Methods Res. 2017 Mar;46(2):155-188. doi: 10.1177/0049124115582272. Epub 2015 May 14.
Randomized controlled trials (RCTs) and quasi-experimental designs like regression discontinuity (RD) designs, instrumental variable (IV) designs, and matching and propensity score (PS) designs are frequently used for inferring causal effects. It is well known that the features of these designs facilitate the identification of a causal estimand and, thus, warrant a causal interpretation of the estimated effect. In this article, we discuss and compare the identifying assumptions of quasi-experiments using causal graphs. The increasing complexity of the causal graphs as one switches from an RCT to RD, IV, or PS designs reveals that the assumptions become stronger as the researcher's control over treatment selection diminishes. We introduce limiting graphs for the RD design and conditional graphs for the latent subgroups of com-pliers, always takers, and never takers of the IV design, and argue that the PS is a collider that offsets confounding bias via collider bias.
随机对照试验（RCT）以及诸如回归间断（RD）设计、工具变量（IV）设计、匹配和倾向得分（PS）设计等准实验设计经常用于推断因果效应。众所周知，这些设计的特征有助于识别因果估计量，因此保证了对估计效应的因果解释。在本文中，我们使用因果图讨论并比较准实验的识别假设。当从RCT转向RD、IV或PS设计时，因果图的复杂性不断增加，这表明随着研究者对治疗选择的控制减弱，假设变得更强。我们引入了RD设计的极限图以及IV设计中依从者、总是接受者和从不接受者的潜在亚组的条件图，并认为倾向得分是一个对撞器，它通过对撞器偏差抵消混杂偏差。
