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.

翻译：结构嵌套均值模型（SNMMs）是一种用于估计随时间变化的处理效应的原理性方法。SNMMs的一个独特优势在于能够将处理序列随时间推移的联合效应分解为局部的、时间特定的“脉冲效应”。这种分解通过增量效应增强了可解释性，并使得无需重新计算即可高效离线评估最优处理策略。然而，目前缺乏针对SNMMs的神经网络框架，因其固有的序列g估计方案阻碍了端到端、基于梯度的训练。本文提出DeepBlip，首个用于SNMMs的神经网络框架，通过一种新颖的双重优化技巧克服了这一限制，实现了所有脉冲函数的同步学习。我们的DeepBlip无缝集成LSTM或Transformer等序列神经网络，以捕捉复杂的时间依赖性。通过设计，该方法能正确调整时变混杂因素以产生无偏估计，其Neyman正交损失函数确保了对干扰模型误设的鲁棒性。最后，我们在多个临床数据集上评估了DeepBlip，其性能达到了当前最优水平。