We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the counterfactual mean of outcome under dynamic treatment policies in longitudinal problem settings. Our approach utilizes a transformer architecture with heterogeneous type embedding trained using temporal-difference learning. After obtaining an initial estimate using the transformer, following the targeted minimum loss-based likelihood estimation (TMLE) framework, we statistically corrected for the bias commonly associated with machine learning algorithms. Furthermore, our method also facilitates statistical inference by enabling the provision of 95% confidence intervals grounded in asymptotic statistical theory. Simulation results demonstrate our method's superior performance over existing approaches, particularly in complex, long time-horizon scenarios. It remains effective in small-sample, short-duration contexts, matching the performance of asymptotically efficient estimators. To demonstrate our method in practice, we applied our method to estimate counterfactual mean outcomes for standard versus intensive blood pressure management strategies in a real-world cardiovascular epidemiology cohort study.

翻译：我们提出深度纵向目标最小损失估计（Deep LTMLE），这是一种在纵向问题设定下估计动态治疗方案反事实结局均值的新方法。该方法采用具有异质性类型嵌入的Transformer架构，并通过时序差分学习进行训练。在利用Transformer获得初始估计后，遵循目标最小损失似然估计框架，对机器学习算法常见的偏差进行了统计修正。此外，本方法还通过基于渐近统计理论提供95%置信区间，促进了统计推断。仿真结果表明，我们的方法在复杂、长时间跨度的场景中展现出优于现有方法的性能，并在小样本、短持续时间情境下保持有效，与渐近有效估计量的表现相当。为验证方法的实用性，我们将其应用于真实世界心血管流行病学队列研究，估计了标准血压管理策略与强化血压管理策略的反事实结局均值。