When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This article introduces for each design the basic rationale, discusses the assumptions required for identifying a causal effect, outlines methods for estimating the effect, and highlights potential validity threats and strategies for dealing with them. Causal estimands and identification results are formalized with the potential outcomes notations of the Rubin causal model.

中文翻译：

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因果推理的准实验设计。

当随机实验不可行时，可以利用准实验设计来评估因果治疗效果。因果推理最强大的准实验设计是回归间断设计、工具变量设计、匹配和倾向评分设计以及比较中断时间序列设计。本文介绍了每种设计的基本原理，讨论了识别因果效应所需的假设，概述了估计效应的方法，并强调了潜在的有效性威胁和应对策略。因果估计值和识别结果用鲁宾因果模型的潜在结果符号形式化。