  Dynamic treatment regimes (DTRs) are used in medicine to tailor sequential
treatment decisions to patients by considering patient heterogeneity. Common
methods for learning optimal DTRs, however, have shortcomings: they are
typically based on outcome prediction and not treatment effect estimation, or
they use linear models that are restrictive for patient data from modern
electronic health records. To address these shortcomings, we develop two novel
methods for learning optimal DTRs that effectively handle complex patient data.
We call our methods DTR-CT and DTR-CF. Our methods are based on a data-driven
estimation of heterogeneous treatment effects using causal tree methods,
specifically causal trees and causal forests, that learn non-linear
relationships, control for time-varying confounding, are doubly robust, and
explainable. To the best of our knowledge, our paper is the first that adapts
causal tree methods for learning optimal DTRs. We evaluate our proposed methods
using synthetic data and then apply them to real-world data from intensive care
units. Our methods outperform state-of-the-art baselines in terms of cumulative
regret and percentage of optimal decisions by a considerable margin. Our work
improves treatment recommendations from electronic health record and is thus of
direct relevance for personalized medicine.
