DeepBlip: Estimating Conditional Average Treatment Effects Over Time

2025-11-18

AI Summary

Structural nested mean models (SNMMs) are difficult to train end-to-end in neural networks due to their conventional sequential g-estimation procedure, which obstructs gradient propagation. Method: This paper introduces DeepBlip—the first neuralized SNMM framework—featuring a dual-optimization mechanism that jointly learns all blip functions and incorporates Neyman-orthogonal loss to enhance robustness against misspecification of the nuisance (interference) model. Architecturally, DeepBlip integrates LSTM and Transformer components to capture complex temporal dependencies and adjust for time-varying confounding. Contribution/Results: Evaluated across multiple clinical datasets, DeepBlip significantly outperforms existing methods in offline evaluation of dynamic treatment regimes, achieving state-of-the-art performance in conditional average treatment effect (CATE) estimation.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Learning & Optimization for NLP
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Structural nested mean models (SNMMs) are a principled approach to estimate the treatment effects over time. A particular strength of SNMMs is to break the joint effect of treatment sequences over time into localized, time-specific ``blip effects''. This decomposition promotes interpretability through the incremental effects and enables the efficient offline evaluation of optimal treatment policies without re-computation. However, neural frameworks for SNMMs are lacking, as their inherently sequential g-estimation scheme prevents end-to-end, gradient-based training. Here, we propose DeepBlip, the first neural framework for SNMMs, which overcomes this limitation with a novel double optimization trick to enable simultaneous learning of all blip functions. Our DeepBlip seamlessly integrates sequential neural networks like LSTMs or transformers to capture complex temporal dependencies. By design, our method correctly adjusts for time-varying confounding to produce unbiased estimates, and its Neyman-orthogonal loss function ensures robustness to nuisance model misspecification. Finally, we evaluate our DeepBlip across various clinical datasets, where it achieves state-of-the-art performance.

Problem

• Estimating conditional average treatment effects over time
• Enabling end-to-end neural training for structural nested models
• Overcoming sequential g-estimation limitations with double optimization

Innovation

• First neural framework for structural nested mean models
• Uses double optimization trick for simultaneous blip learning
• Integrates LSTMs or transformers for temporal dependencies