Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation

• URL: http://arxiv.org/abs/2602.12379v1
• Date: Thu, 12 Feb 2026 20:16:27 GMT
• Title: Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation
• Authors: Wenxin Chen, Weishen Pan, Kyra Gan, Fei Wang,
• Abstract summary: Estimating longitudinal treatment effects is essential for sequential decision-making but is challenging due to treatment-confounder feedback.<n>We propose D3-Net, a framework that mitigates error propagation in ICE training and then applies a robust final correction.<n>Our model, D3-Net, robustly reduces bias and variance across different horizons, counterfactuals, and time-varying confoundings.
• Score: 13.1234934131475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 representations against corruption by noisy pseudo-outcomes, 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.

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