Model-agnostic meta-learners for estimating heterogeneous treatment effects over time

TL;DR

This paper introduces a set of model-agnostic meta-learners for estimating time-varying heterogeneous treatment effects, enabling flexible use with various machine learning models and providing theoretical and empirical validation.

Contribution

It is the first to develop comprehensive meta-learners for dynamic treatment effect estimation over time, adaptable to any machine learning model.

Findings

Meta-learners outperform existing methods in simulations.

Theoretical analysis clarifies when to prefer specific learners.

Numerical experiments validate the proposed approach.

Abstract

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.