  Interpretability is a key concern in estimating heterogeneous treatment
effects using machine learning methods, especially for healthcare applications
where high-stake decisions are often made. Inspired by the Predictive,
Descriptive, Relevant framework of interpretability, we propose causal rule
learning which finds a refined set of causal rules characterizing potential
subgroups to estimate and enhance our understanding of heterogeneous treatment
effects. Causal rule learning involves three phases: rule discovery, rule
selection, and rule analysis. In the rule discovery phase, we utilize a causal
forest to generate a pool of causal rules with corresponding subgroup average
treatment effects. The selection phase then employs a D-learning method to
select a subset of these rules to deconstruct individual-level treatment
effects as a linear combination of the subgroup-level effects. This helps to
answer an ignored question by previous literature: what if an individual
simultaneously belongs to multiple groups with different average treatment
effects? The rule analysis phase outlines a detailed procedure to further
analyze each rule in the subset from multiple perspectives, revealing the most
promising rules for further validation. The rules themselves, their
corresponding subgroup treatment effects, and their weights in the linear
combination give us more insights into heterogeneous treatment effects.
Simulation and real-world data analysis demonstrate the superior performance of
causal rule learning on the interpretable estimation of heterogeneous treatment
effect when the ground truth is complex and the sample size is sufficient.
