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 compliers, always takers, and never takers of the IV design, and argue that the PS is a collider that offsets confounding bias via collider bias.

中文翻译：

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准实验设计的图形模型

随机对照试验 (RCT) 和准实验设计，如回归不连续性 (RD) 设计、工具变量 (IV) 设计以及匹配和倾向评分 (PS) 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，从而保证对估计效果的因果解释。在本文中，我们使用因果图讨论和比较准实验的识别假设。随着研究人员从 RCT 切换到 RD、IV 或 PS 设计，因果图变得越来越复杂，这表明随着研究人员对治疗选择的控制减弱，假设变得更强。我们引入了 RD 设计的限制图和潜在的编译器子组的条件图，总是接受者，