CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect
This addresses the need for interpretable subgroup identification in causal inference for applications like precision medicine and personalized advertising, representing a novel method for a known bottleneck.
The paper tackled the problem of providing explicit subgroup descriptions in heterogeneous treatment effect estimation, proposing CURLS, which achieved 16.1% higher estimated effects, 13.8% higher true effects, and 12.0% smaller variance compared to state-of-the-art methods.
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/.