Factorial Difference-in-Differences
将经典双重差分法扩展到事件影响所有单位的场景，提出因子双重差分设计，区分效应修饰与因果调节，并给出识别条件与实证应用。
We formulate factorial difference-in-differences (FDID), a research design that extends canonical difference-in-differences (DID) to settings in which an event affects all units. In many panel data applications, researchers exploit cross-sectional variation in a baseline factor alongside temporal variation in the event, but the corresponding estimand is often implicit and the justification for applying the DID estimator remains unclear. We frame FDID as a factorial design with two factors, the baseline factor G and the exposure level Z, and define effect modification and causal moderation as the associative and causal effects of G on the effect of Z, respectively. Under standard DID assumptions of no anticipation and parallel trends, the DID estimator identifies effect modification but not causal moderation. Identifying the latter requires an additional factorial parallel trends assumption, that is, mean independence between G and potential outcome trends. We extend the framework to conditionally valid assumptions and regression-based implementations, and further to repeated cross-sectional data and continuous G. We demonstrate the framework with an empirical application on the role of social capital in famine relief in China.