  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.
