提出两种去偏前端学习器，高效估计因果异质效应。
Debiased Front-Door Learners for Heterogeneous Effects
- 基于前端调整机制，通过中介变量分离混淆影响。
- 在合成与真实数据中均实现低偏差、高样本效率的估计。
- 适合处理存在未观测混淆但中介变量干净的因果场景。
在处理与结果共享未观测混淆因子但可观测中介变量无混淆的观察性研究中，前端（FD）调整可通过中介变量识别因果效应。本文研究了在FD识别下的异质处理效应（HTE），提出两种去偏学习器：FD-DR-Learner和FD-R-Learner。在明确样本分割、重叠有界、矩条件及阶段学习假设下，证明了FD-DR满足乘积误差界，FD-R满足阶段误差分解；当相关干扰余项不超过目标或阶段最优项时，可得条件准最优推论。通过误差分析验证其去偏性，并在合成数据与真实案例（使用FARS数据集研究初等安全带法律）中展示稳健性能。结果表明，在假设合理时，该方法能可靠高效地估计FD场景下的HTE。代码已公开于https://github.com/yonghanjung/FD-CATE。
原文摘要 · Abstract (English)
In observational settings where treatment and outcome share unmeasured confounders but an observed mediator remains unconfounded, the front-door (FD) adjustment identifies causal effects through the mediator. We study the heterogeneous treatment effect (HTE) under FD identification and introduce two debiased learners: FD-DR-Learner and FD-R-Learner. Under explicit sample-splitting, bounded-overlap, moment, and stage-learning assumptions, we show that FD-DR satisfies a product-error bound and FD-R satisfies a stage-error decomposition; these results yield conditional quasi-oracle corollaries when the relevant nuisance remainders are no larger than the target or stage oracle terms. We provide error analyses establishing this debiasedness and demonstrate robust empirical performance in synthetic studies and a real-world case study of primary seat-belt laws using Fatality Analysis Reporting System (FARS) dataset. Together, these results indicate that the proposed learners can deliver reliable and sample-efficient HTE estimates in FD scenarios when the stated assumptions are credible. The implementation is available at https://github.com/yonghanjung/FD-CATE.
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