Difference in Differences with Time-Varying Covariates
含时变协变量的双重差分AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究双重差分的因果效应识别，并讨论两期面板中的双向固定效应回归。
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含时变协变量的双重差分
- 研究问题
- 在给定协变量后平行趋势成立的双重差分框架中，如何识别和估计处理效应，尤其当协变量随时间变化或受处理影响时？
- 方法与思路
- 作者分别针对协变量是否受处理影响提出策略，并分析含时变回归量的双向固定效应回归，构造双重稳健及回归调整、插补方法。
- 材料陈述的贡献
- 摘要称，即使只有两期，常见双向固定效应回归也可能因协变量水平、效应异质性或函数形式限制而误导，并提出相应替代方法。
- 阅读关注点
- 应核对两类协变量情形的识别条件、目标效应及条件平行趋势的定义，并检查双重稳健性质与回归调整方法各自依赖的条件。
- 阅读边界
- 这是 arXiv 预印本，未核验同行评审或期刊接收；摘要未给出完整识别条件，不能据此认为处理后的协变量可直接作为控制变量。
原文摘要
This paper considers identification and estimation of causal effect parameters from participating in a binary treatment in a difference in differences (DID) setup when the parallel trends assumption holds after conditioning on observed covariates. Relative to existing work in the econometrics literature, we consider the case where the value of covariates can change over time and, potentially, where participating in the treatment can affect the covariates themselves. We propose new empirical strategies in both cases. We also consider two-way fixed effects (TWFE) regressions that include time-varying regressors, which is the most common way that DID identification strategies are implemented under conditional parallel trends. We show that, even in the case with only two time periods, these TWFE regressions are not generally robust to (i) time-varying covariates being affected by the treatment, (ii) treatment effects and/or paths of untreated potential outcomes depending on the level of time-varying covariates in addition to only the change in the covariates over time, (iii) treatment effects and/or paths of untreated potential outcomes depending on time-invariant covariates, (iv) treatment effect heterogeneity with respect to observed covariates, and (v) violations of strong functional form assumptions, both for outcomes over time and the propensity score, that are unlikely to be plausible in most DID applications. Thus, TWFE regressions can deliver misleading estimates of causal effect parameters in a number of empirically relevant cases. We propose both doubly robust estimands and regression adjustment/imputation strategies that are robust to these issues while not being substantially more challenging to implement.