Conditional Front-door Adjustment for Heterogeneous Treatment Assignment Effect Estimation Under Non-adherence

2025/05/08 by Chen, Winston, Trenton Chang, Chang, Trenton +2
Computer Science · Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2505.05677

openalex publication_date 2025/05/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Estimates of heterogeneous treatment assignment effects can inform treatment decisions. Under the presence of non-adherence (e.g., patients do not adhere to their assigned treatment), both the standard backdoor adjustment (SBD) and the conditional front-door adjustment (CFD) can recover unbiased estimates of the treatment assignment effects. However, the estimation variance of these approaches may vary widely across settings, which remains underexplored in the literature. In this work, we demonstrate theoretically and empirically that CFD yields lower-variance estimates than SBD when the true effect of treatment assignment is small (i.e., assigning an intervention leads to small changes in patients' future outcome). Additionally, since CFD requires estimating multiple nuisance parameters, we introduce LobsterNet, a multi-task neural network that implements CFD with joint modeling of the nuisance parameters. Empirically, LobsterNet reduces estimation error across several semi-synthetic and real-world datasets compared to baselines. Our findings suggest CFD with shared nuisance parameter modeling can improve treatment assignment effect estimation under non-adherence.

Citations

• Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
• End-to-End Balancing for Causal Continuous Treatment-Effect Estimation
• On Inductive Biases for Heterogeneous Treatment Effect Estimation
• Causal Effect Inference for Structured Treatments
• Towards optimal doubly robust estimation of heterogeneous causal effects
• Estimating Treatment Effects with Observed Confounders and Mediators
• Adapting Neural Networks for the Estimation of Treatment Effects
• Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approach
• Estimation of causal effects with multiple treatments: a review and new ideas
• Double/Debiased Machine Learning for Treatment and Causal Parameters
• Bayesian Nonparametric Modeling for Causal Inference

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