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Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation

arXiv:2602.12379v1 Announce Type: new 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

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Wenxin Chen, Weishen Pan, Kyra Gan, Fei Wang · · 1 min read · 3 views
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arXiv:2602.12379v1 Announce Type: new 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.

Executive Summary

The article introduces D3-Net, a novel framework designed to enhance the estimation of longitudinal treatment effects by addressing the challenges posed by treatment-confounder feedback and error propagation in Iterative Conditional Expectation (ICE) G-computation. D3-Net employs Sequential Doubly Robust (SDR) pseudo-outcomes to mitigate error propagation during the learning phase and utilizes a multi-task Transformer with a covariate simulator head for robust representation learning. The framework concludes with a targeted debiasing step using Longitudinal Targeted Minimum Loss-Based Estimation (LTMLE) to ensure optimal finite-sample properties. The authors demonstrate that D3-Net significantly reduces bias and variance compared to existing ICE-based estimators across various scenarios.

Key Points

  • ▸ D3-Net addresses error propagation in ICE G-computation by using SDR pseudo-outcomes.
  • ▸ A multi-task Transformer with a covariate simulator head is employed for robust representation learning.
  • ▸ Final estimation is performed using LTMLE on original outcomes for targeted debiasing.
  • ▸ Comprehensive experiments show D3-Net's superiority in reducing bias and variance.

Merits

Innovative Framework

D3-Net introduces a novel approach to mitigate error propagation in ICE G-computation, enhancing the accuracy of longitudinal treatment effect estimation.

Robust Representation Learning

The use of a multi-task Transformer with a covariate simulator head provides robust representation learning, reducing the impact of noisy pseudo-outcomes.

Targeted Debiasing

The final stage of LTMLE ensures optimal finite-sample properties, making the estimates more reliable and robust.

Demerits

Complexity

The framework's complexity may pose challenges in implementation and computational efficiency, particularly for practitioners with limited resources.

Data Requirements

The effectiveness of D3-Net may be contingent on the availability of high-quality, longitudinal data, which may not always be feasible.

Expert Commentary

The article presents a significant advancement in the field of longitudinal treatment effect estimation. By addressing the critical issue of error propagation in ICE G-computation, D3-Net offers a more robust and accurate framework for causal inference. The use of SDR pseudo-outcomes and a multi-task Transformer with a covariate simulator head demonstrates a sophisticated approach to representation learning, ensuring that the final estimates are both reliable and optimal. The comprehensive experiments validate the framework's superiority over existing methods, highlighting its potential for practical applications in healthcare and policy. However, the complexity and data requirements of D3-Net may pose challenges for widespread adoption. Future research could focus on simplifying the framework and exploring its applicability to different domains beyond healthcare.

Recommendations

  • ✓ Further research should explore the scalability and computational efficiency of D3-Net to make it more accessible for practitioners.
  • ✓ Investigating the applicability of D3-Net in other domains, such as economics and social sciences, could broaden its impact and utility.

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