Debiased Front-Door Learners for Heterogeneous Effects

• URL: http://arxiv.org/abs/2509.22531v1
• Date: Fri, 26 Sep 2025 16:11:14 GMT
• Title: Debiased Front-Door Learners for Heterogeneous Effects
• Abstract summary: We study the heterogeneous treatment effect (HTE) under front-door (FD) identification.<n>We introduce two debiased learners: FD-DR-Learner and FD-R-Learner.<n>We demonstrate robust empirical performance in synthetic studies and a real-world case study of primary seat-belt laws.
• Score: 12.13022422078211
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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