AI 中文总结
研究交错采用的协变量条件平均治疗效果估计问题，提出用固定效应因果森林估计，在每个树节点内差分单位和时期效应消除混杂，蒙特卡罗实验验证其有效性，应用于医疗补助扩展取得未参保率下降等成果。
AI 中文摘要
交错采用的差分法通过比较各队列与未接受治疗的单位来识别组时间平均治疗效果ATT(g,t)，避免了异质性效应时双向固定效应估计量的“禁止比较”偏差。本文研究了该对象的协变量条件版本tau_{g,t}(x)，并用固定效应因果森林进行估计。在每个(g,t)比较块内，结果和治疗在每个树节点内的单位和时期固定效应上进行残差化，诚实的因果树根据协变量中的治疗效果异质性进行分割。虽然估计量并非新提出的，但本文提供了一种不同的估计方法。通过在每个树节点内差分单位和时期效应来消除混杂因素，将固定效应残差化方法引入Callaway-Sant'Anna组时间结构。蒙特卡罗实验表明，该估计量是在交错时间和队列效应下唯一保持无偏且正确覆盖总体效应的基于森林的方法。本文还将该方法应用于Callaway-Sant'Anna最低工资面板进行验证，并应用于ACA医疗补助扩展的县级交错推出，发现未参保率平均下降2.25个百分点，且较贫困和低收入县获得了更多的保险覆盖。
英文摘要
Difference-in-differences with staggered adoption identifies group-time average treatment effects ATT(g,t) by comparing each cohort to units not yet treated, which avoids the "forbidden comparisons" that bias two-way fixed-effects estimators when effects are heterogeneous. This paper studies the covariate-conditional version of that object, tau_{g,t}(x), and estimates it with a fixed-effects causal forest. Within each (g,t) comparison block, the outcome and treatment are residualized on unit and period fixed effects inside each tree node, and honest causal trees split on treatment-effect heterogeneity in the covariates. The estimand is not new: Hatamyar, Kreif, Rocha and Huber (2023) introduced it using a doubly-robust R-learner, and Imai, Qin and Yanagi (2023) study it for a single continuous covariate. What we add is a different way to estimate it. Where those methods remove confounding by modeling nuisance functions, we remove it by differencing out unit and period effects within each tree node, following the fixed-effects residualization of Kattenberg, Scheer and Thiel (2023) and Gavrilova, Langorgen and Zoutman (2025) and carrying it into the Callaway-Sant'Anna group-time structure. In Monte Carlo experiments the estimator is the only forest-based method that stays unbiased and correctly covered for the overall effect under staggered timing with cohort-varying effects; two-way fixed effects and a pooled causal forest inherit large forbidden-comparison bias. We apply the method to the Callaway-Sant'Anna minimum-wage panel as a validation and to the staggered county-level rollout of the ACA Medicaid expansion, where it recovers an average 2.25 percentage-point fall in the uninsured rate and a conditional surface on which poorer and lower-income counties gained substantially more coverage -- heterogeneity measured along socioeconomic covariates that are not lags of the outcome.
Comments16 pages, 1 figure, 7 tables. JEL: C14, C21, C23, I13, J38
