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