找出治疗效果更高的可解释子群体，用简单规则描述
An Algorithm for Identifying Interpretable Subgroups With Elevated Treatment Effects
- 用逻辑规则（如条件A且条件B）定义可理解的子群体
- 通过权衡子群大小与效果大小，得到多组最优规则集
- 适合需要可解释决策依据的医疗或政策研究
我们提出一种算法，用于在已估计个体或条件平均治疗效应（CATE）的基础上，识别具有较高治疗效果的可解释子群体。子群体由“规则集”表征——形式为（条件A 且 条件B）或（条件C），既能捕捉高阶交互作用，又保持可读性。该方法补充了现有高维、难解释的CATE估计方法，从拟合模型中提炼关键信息，辅助决策制定、政策实施与科学理解。我们设计了一个权衡子群大小与效应大小的目标函数，调节超参数可获得一组帕累托最优规则集，彼此无绝对优劣。通过样本分割可实现有效推断。我们在模拟和真实数据中展示了该方法的实用性与局限性。
原文摘要 · Abstract (English)
We introduce an algorithm for identifying interpretable subgroups with elevated treatment effects, given an estimate of individual or conditional average treatment effects (CATE). Subgroups are characterized by ``rule sets'' -- easy-to-understand statements of the form (Condition A AND Condition B) OR (Condition C) -- which can capture high-order interactions while retaining interpretability. Our method complements existing approaches for estimating the CATE, which often produce high dimensional and uninterpretable results, by summarizing and extracting critical information from fitted models to aid decision making, policy implementation, and scientific understanding. We propose an objective function that trades-off subgroup size and effect size, and varying the hyperparameter that controls this trade-off results in a ``frontier'' of Pareto optimal rule sets, none of which dominates the others across all criteria. Valid inference is achievable through sample splitting. We demonstrate the utility and limitations of our method using simulated and empirical examples.
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