Valid Inference when Testing Violations of Parallel Trends for Difference-in-Differences
检验双重差分平行趋势违背时的有效推断AI 解读 ↓
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检验双重差分平行趋势违背时的有效推断
- 研究问题
- 双重差分研究先检验处理前趋势、再报告处理效应时，如何避免预检验导致的偏差与覆盖不足，并获得通过检验后的有效区间？
- 方法与思路
- 作者提出简单的预检验与配套置信区间，通过条件外推假设连接处理前可识别的趋势违背和处理后不可识别的趋势违背。
- 材料陈述的贡献
- 摘要称，在温和分离条件下，预检验具有一致性，因果效应区间在通过检验的条件下覆盖有效，并提供模拟和两项应用说明。
- 阅读关注点
- 应核对条件外推假设与分离条件的具体含义、条件覆盖的目标，以及新方法如何处理低功效和预检验筛选带来的影响。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未获核实；摘要未给出具体条件或区间公式，通过处理前检验仍不能直接验证反事实平行趋势。
原文摘要
The difference-in-differences (DID) research design is a key identification strategy which allows researchers to estimate causal effects under the parallel trends assumption. While the parallel trends assumption is counterfactual and cannot be tested directly, researchers often examine pre-treatment periods to check whether the time trends are parallel before treatment is administered. A recent literature has shown that existing preliminary tests have adverse effects on conventional statistical methods for estimation and inference, including low power, bias, and undercoverage. In this paper, we describe simple preliminary tests and corresponding confidence intervals for the causal effect which overcome these issues. Under mild separation conditions, the preliminary test is shown to be consistent and the confidence intervals for the causal effect have valid coverage conditional on passing the test. Our results hold under what we refer to as the conditional extrapolation assumption, which posits a relationship between the unidentified post-treatment violation of parallel trends and the identified pre-treatment violations. We view the conditional extrapolation assumption as one formalization of the assumption which is implicitly held when conducting a preliminary test for parallel trends. To illustrate the performance of the proposed methods, we use synthetic data as well as data on recentralization of public services in Vietnam and right-to-carry laws in Virginia.