Debiased Front-Door Learners for Heterogeneous Effects
This provides reliable, sample-efficient causal effect estimation for researchers in fields like public policy or healthcare where unmeasured confounding is common.
The paper tackles the problem of estimating heterogeneous treatment effects in observational studies with unmeasured confounders using front-door adjustment, introducing FD-DR-Learner and FD-R-Learner that achieve quasi-oracle rates even with slow-converging nuisance functions (n^-1/4).
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. Both attain fast, quasi-oracle rates (i.e., performance comparable to an oracle that knows the nuisances) even when nuisance functions converge as slowly as n^-1/4. We provide error analyses establishing 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 deliver reliable and sample-efficient HTE estimates in FD scenarios. The implementation is available at https://github.com/yonghanjung/FD-CATE. Keywords: Front-door adjustment; Heterogeneous treatment effects; Debiased learning; Quasi-oracle rates; Causal inference.