在患者不遵从治疗时，新方法能更稳定地估计治疗效果。
Conditional Front-door Adjustment for Heterogeneous Treatment Assignment Effect Estimation Under Non-adherence
- 用多任务神经网络联合建模干扰项，提升估计精度
- 当治疗影响较小时，该方法方差显著低于传统方法
- 适合医学试验中存在服药不依从的场景
在存在非遵从性（如患者未按医嘱服药）的情况下，标准后门调整（SBD）和条件前门调整（CFD）均可无偏估计治疗分配效应。然而，两者估计方差在不同情境下差异显著，这一问题尚未被充分研究。本文理论与实证表明，当治疗分配的真实效应较小时（即分配干预对患者未来结果影响微弱），CFD 的估计方差低于 SBD。由于 CFD 需要估计多个干扰参数，我们提出 LobsterNet——一种多任务神经网络，实现干扰参数的联合建模。在多个半合成及真实世界数据集上的实验显示，LobsterNet 相比基线方法显著降低估计误差。结果表明，在非遵从条件下，通过共享干扰参数建模可提升治疗分配效应估计性能。
原文摘要 · Abstract (English)
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.
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