  The increasing scientific attention given to precision medicine based on
real-world data has led many recent studies to clarify the relationships
between treatment effects and patient characteristics. However, this is
challenging because of ubiquitous heterogeneity in the treatment effect for
individuals and the real-world data on their background being complex and
noisy. Because of their flexibility, various heterogeneous treatment effect
(HTE) machine learning (ML) estimation methods have been proposed. However,
most ML methods incorporate black-box models that hamper direct interpretation
of the interrelationships between individuals' characteristics and the
treatments' effects. This study proposes an ML method for estimating HTE based
on the rule ensemble method termed RuleFit. The main advantage of RuleFit are
interpretability and accuracy. However, HTEs are always defined in the
potential outcome framework, and RuleFit cannot be applied directly. Thus, we
modified RuleFit and proposed a method to estimate HTEs that directly interpret
the interrelationships among the individuals' features from the model.
