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

TL;DR

This paper introduces LobsterNet, a neural network that implements the conditional front-door adjustment to estimate heterogeneous treatment effects more accurately under non-adherence, especially when true effects are small.

Contribution

It demonstrates that CFD can have lower variance than SBD in certain settings and proposes LobsterNet for joint modeling of nuisance parameters to improve estimation accuracy.

Findings

LobsterNet reduces estimation error in semi-synthetic datasets.

CFD yields lower-variance estimates than SBD when true effects are small.

Shared nuisance parameter modeling improves treatment effect estimation.

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.