A Fixed-Effects Causal Forest for Staggered Adoption, with an Application to Medicaid Expansion
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用于交错实施的固定效应因果森林及其医保扩张应用
- 研究问题
- 在政策交错实施且效应随队列和协变量变化时，如何估计条件组别—时间治疗效应，并避免不恰当比较带来的偏差？
- 方法与思路
- 作者在各组别—时间比较块中，以尚未处理单位为对照，在树节点内剔除个体与时期固定效应，并用诚实树寻找异质性。
- 材料陈述的贡献
- 摘要明确贡献是既有目标量的新估计方法，报告模拟中的偏差与覆盖表现，并展示医保扩张效应的社会经济异质性。
- 阅读关注点
- 应核对条件平行趋势、节点内残差化的适用条件、诚实分样与标准误构造，以及森林比较的模拟设计和对照方法。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审与期刊接收未核实；摘要中唯一无偏且覆盖正确的说法仅限所比较的模拟设置。
原文摘要
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.