Kim Yongnam, Steiner Peter
Department of Educational Psychology, University of Wisconsin-Madison.
Educ Psychol. 2016;51(3-4):395-405. doi: 10.1080/00461520.2016.1207177. Epub 2016 Sep 2.
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.
当随机实验不可行时，可以采用准实验设计来评估因果处理效应。用于因果推断的最强准实验设计是回归断点设计、工具变量设计、匹配和倾向得分设计以及比较中断时间序列设计。本文针对每种设计介绍了基本原理，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以及应对这些威胁的策略。因果估计量和识别结果用鲁宾因果模型的潜在结果符号进行形式化。
