Estimating dynamic treatment effects is a crucial endeavor in causal inference, particularly when confronted with high-dimensional confounders. Doubly robust (DR) approaches have emerged as promising tools for estimating treatment effects due to their flexibility. However, we showcase that the traditional DR approaches that only focus on the DR representation of the expected outcomes may fall short of delivering optimal results. In this paper, we propose a novel DR representation for intermediate conditional outcome models that leads to superior robustness guarantees. The proposed method achieves consistency even with high-dimensional confounders, as long as at least one nuisance function is appropriately parametrized for each exposure time and treatment path. Our results represent a significant step forward as they provide new robustness guarantees. The key to achieving these results is our new DR representation, which offers superior inferential performance while requiring weaker assumptions. Lastly, we confirm our findings in practice through simulations and a real data application.

翻译：动态治疗效应的估计是因果推断中的关键任务，尤其当面临高维混杂因素时。双重稳健（DR）方法因其灵活性而成为估计治疗效应的有力工具。然而，我们指出，传统DR方法仅关注预期结果的DR表示，可能无法达到最优效果。本文提出了一种针对中间条件结果模型的新型DR表示，该方法能提供更优的稳健性保证。即使存在高维混杂因素，只要每个暴露时间和治疗路径中至少有一个干扰函数被恰当参数化，所提方法即可实现一致性估计。我们的结果代表了重要进展，因为其提供了新的稳健性保证。实现这些结果的关键在于新型DR表示——它在放宽假设条件的同时，实现了更优的推断性能。最后，我们通过模拟实验和实际数据应用验证了这些发现的实用性。