Estimating heterogeneous treatment effects (HTEs) over time is crucial in many disciplines such as personalized medicine. For example, electronic health records are commonly collected over several time periods and then used to personalize treatment decisions. Existing works for this task have mostly focused on model-based learners (i.e., learners that adapt specific machine-learning models). In contrast, model-agnostic learners -- so-called meta-learners -- are largely unexplored. In our paper, we propose several meta-learners that are model-agnostic and thus can be used in combination with arbitrary machine learning models (e.g., transformers) to estimate HTEs over time. Here, our focus is on learners that can be obtained via weighted pseudo-outcome regressions, which allows for efficient estimation by targeting the treatment effect directly. We then provide a comprehensive theoretical analysis that characterizes the different learners and that allows us to offer insights into when specific learners are preferable. Finally, we confirm our theoretical insights through numerical experiments. In sum, while meta-learners are already state-of-the-art for the static setting, we are the first to propose a comprehensive set of meta-learners for estimating HTEs in the time-varying setting.

翻译：估计随时间变化的异质性处理效应（HTEs）在个性化医疗等诸多领域至关重要。例如，电子健康记录通常跨多个时间段收集，随后用于个性化治疗决策。现有研究多集中于基于模型的学习器（即适配特定机器学习模型的学习器）。相比之下，模型无关学习器——即所谓元学习器——尚未得到充分探索。本文提出若干种模型无关的元学习器，可与任意机器学习模型（如Transformer）结合使用以估计随时间变化的HTEs。我们重点关注通过加权伪结果回归获得的学习器，该方法通过直接针对处理效应进行估计来实现高效计算。随后我们提供了系统的理论分析，以刻画不同学习器的特性，从而揭示特定学习器的适用场景。最后通过数值实验验证了理论分析的结论。总体而言，尽管元学习器在静态场景中已处于领先地位，本研究首次针对时变场景提出了一套完整的HTE估计元学习器体系。