  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 https://osf.io/zwp2k/.
