In causal inference, estimating heterogeneous treatment effects (HTE) is
critical for identifying how different subgroups respond to interventions, with
broad applications in fields such as precision medicine and personalized
advertising. Although HTE estimation methods aim to improve accuracy, how to
provide explicit subgroup descriptions remains unclear, hindering data
interpretation and strategic intervention management. In this paper, we propose
CURLS, a novel rule learning method leveraging HTE, which can effectively
describe subgroups with significant treatment effects. Specifically, we frame
causal rule learning as a discrete optimization problem, finely balancing
treatment effect with variance and considering the rule interpretability. We
design an iterative procedure based on the minorize-maximization algorithm and
solve a submodular lower bound as an approximation for the original.
Quantitative experiments and qualitative case studies verify that compared with
state-of-the-art methods, CURLS can find subgroups where the estimated and true
effects are 16.1% and 13.8% higher and the variance is 12.0% smaller, while
maintaining similar or better estimation accuracy and rule interpretability.
Code is available at this https URL

本文提出了一种名为 CURLS 的新型规则学习方法，利用异质性治疗效果 (HTE) 有效地描述具有显著治疗效果的亚群体。通过在离散优化问题中精确平衡治疗效果与方差，并考虑规则的可解释性，我们设计了基于次优化 - 最大化算法的迭代过程，并将次模的下界作为原问题的近似解。量化实验和定性案例研究验证，与现有方法相比，CURLS 能够找到估计效果和真实效果分别提高了 16.1% 和 13.8%，方差减少了 12.0%，同时保持了类似或更好的估计准确性和规则可解释性。