Estimating dynamic treatment effects is essential across various disciplines, offering nuanced insights into the time-dependent causal impact of interventions. However, this estimation presents challenges due to the "curse of dimensionality" and time-varying confounding, which can lead to biased estimates. Additionally, correctly specifying the growing number of treatment assignments and outcome models with multiple exposures seems overly complex. Given these challenges, the concept of double robustness, where model misspecification is permitted, is extremely valuable, yet unachieved in practical applications. This paper introduces a new approach by proposing novel, robust estimators for both treatment assignments and outcome models. We present a "sequential model double robust" solution, demonstrating that double robustness over multiple time points can be achieved when each time exposure is doubly robust. This approach improves the robustness and reliability of dynamic treatment effects estimation, addressing a significant gap in this field.

翻译：估计动态治疗效果对于多个学科至关重要，能够深入揭示干预措施随时间变化的因果影响。然而，这一估计过程面临“维度诅咒”和时变混杂因素的挑战，可能导致有偏估计。此外，随着治疗分配和结果模型数量的增加，正确指定这些多暴露变量模型显得异常复杂。面对这些挑战，允许模型错误设定的双重稳健性概念极具价值，但在实际应用中尚未实现。本文提出了一种新颖方法，针对治疗分配和结果模型分别提出了稳健的估计器。我们提出了一种“序列模型双重稳健”解决方案，证明当每个时间点的暴露模型具有双重稳健性时，可以在多个时间点上实现双重稳健性。该方法提升了动态治疗效果估计的稳健性与可靠性，弥补了该领域的一项重要空白。