Generalized Difference-in-Differences.
Generalized Difference-in-Differences.
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广义双重差分。
DOI：
10.1097/ede.0000000000001568
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
TchetgenTchetgen,EricJ
中科院分区：
文献类型：
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作者：
Richardson,DavidB;Ye,Ting;TchetgenTchetgen,EricJ
差异性(DID)分析被用于各种研究领域，作为评估政策、计划、干预或环境危害(下称治疗)的因果影响的策略。该方法提供了一种使用观察性(即非随机化)数据估计治疗因果效应的策略，其中每个研究单元的结果都在治疗前后进行了测量。为了确定因果关系，DID分析依赖于一种假设，即在治疗前阶段混淆治疗效果等同于混淆治疗后阶段的治疗效果。我们提出了另一种方法，可以在不同的识别条件下产生因果效应的识别，而不是通常要求的DID。拟议的方法，我们称之为广义DID，有可能用于跨许多学科的常规政策评估，因为它本质上结合了两种流行的准实验设计，利用了它们的优势，同时放松了它们通常的假设。我们给出了确定因果效应的条件的形式描述，用模拟说明了方法，并提供了一个基于Card和Krueger关于新泽西州提高最低工资对就业影响的里程碑式研究的实证例子。
Difference-in-differences (DID) analyses are used in a variety of research areas as a strategy for estimating the causal effect of a policy, program, intervention, or environmental hazard (hereafter, treatment). The approach offers a strategy for estimating the causal effect of a treatment using observational (ie, nonrandomized) data in which outcomes on each study unit have been measured both before and after treatment. To identify a causal effect, a DID analysis relies on an assumption that confounding of the treatment effect in the pretreatment period is equivalent to confounding of the treatment effect in the post treatment period. We propose an alternative approach that can yield identification of causal effects under different identifying conditions than those usually required for DID. The proposed approach, which we refer to as generalized DID, has the potential to be used in routine policy evaluation across many disciplines, as it essentially combines two popular quasiexperimental designs, leveraging their strengths while relaxing their usual assumptions. We provide a formal description of the conditions for identification of causal effects, illustrate the method using simulations, and provide an empirical example based on Card and Krueger’s landmark study of the impact of an increase in minimum wage in New Jersey on employment.