Estimating longitudinal treatment effects is essential for sequential decision-making but is challenging due to treatment-confounder feedback. While Iterative Conditional Expectation (ICE) G-computation offers a principled approach, its recursive structure suffers from error propagation, corrupting the learned outcome regression models. We propose D3-Net, a framework that mitigates error propagation in ICE training and then applies a robust final correction. First, to interrupt error propagation during learning, we train the ICE sequence using Sequential Doubly Robust (SDR) pseudo-outcomes, which provide bias-corrected targets for each regression. Second, we employ a multi-task transformer with a covariate simulator head for auxiliary supervision, regularizing representation learning, and a target network to stabilize training dynamics. For the final estimate, we discard the SDR correction and instead use the uncorrected nuisance models to perform Longitudinal Targeted Minimum Loss-Based Estimation (LTMLE) on the original outcomes. This second-stage, targeted debiasing ensures robustness and optimal finite-sample properties. Comprehensive experiments demonstrate that our model, D3-Net, robustly reduces bias and variance across different horizons, counterfactuals, and time-varying confoundings, compared to existing state-of-the-art ICE-based estimators.

翻译：估计纵向治疗效应对于序列决策至关重要，但由于治疗-混杂反馈机制的存在，这一任务面临挑战。尽管迭代条件期望（ICE）G计算提供了一种理论完备的方法，但其递归结构存在误差传播问题，会破坏学习到的结果回归模型。我们提出D3-Net框架，该框架能够缓解ICE训练中的误差传播，并应用鲁棒的最终修正。首先，为打断学习过程中的误差传播，我们使用序列双稳健（SDR）伪结果训练ICE序列，为每个回归提供偏差校正目标。其次，我们采用多任务Transformer架构，配备协变量模拟器头部进行辅助监督（正则化表示学习）和目标网络（稳定训练动态）。在最终估计阶段，我们舍弃SDR修正，转而使用未校正的干扰模型对原始结果执行纵向目标最小损失估计（LTMLE）。这种第二阶段的目标导向去偏方法确保了鲁棒性和最优有限样本性质。综合实验表明，与现有最先进的ICE基估计器相比，我们的模型D3-Net能够在不同时间范围、反事实情景和时变混杂条件下稳健地降低偏差和方差。