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

Causal Rule Learning: Enhancing the Understanding of Heterogeneous Treatment Effect via Weighted Causal Rules