Highly adaptive Lasso for estimation of heterogeneous treatment effects and treatment recommendation

Abstract The estimation of conditional average treatment effects (CATEs) is an important problem in many applications. Many machine learning-based frameworks for such estimation have been proposed, including meta-learning, causal trees, and causal forests. However, few of these methods are interpretable, while those that do emphasize interpretability often suffer in terms of performance. Here, we propose several methods that build on existing meta-learning algorithms to produce CATE estimates that can be represented as trees. We also describe new methods for the estimation of optimal treatment policies (OTPs), an area where interpretable, auditable treatment decision rules are often desirable. We introduce this method for settings with an arbitrary number of treatment arms. We provide regret rates for the proposed methods and show that they outperform popular methods, both interpretable and not. Finally, we demonstrate the use of our method on both simulated and real data from the Antibiotics for Children with severe Diarrhea trial to create OTPs for antibiotic treatment.