标题： 一种用于识别具有提升治疗效果的可解释子群的算法 显示英文标题

标题： An Algorithm for Identifying Interpretable Subgroups With Elevated Treatment Effects

摘要： 我们引入一种算法，用于在获得个体或条件平均处理效应（CATE）估计的情况下，识别具有提升处理效应的可解释子组。子组由“规则集”来表征——易于理解的语句形式为（条件A AND 条件B）OR（条件C）——这些规则集可以捕捉高阶交互作用，同时保持可解释性。我们的方法通过从拟合的模型中总结和提取关键信息，以辅助决策制定、政策实施和科学理解，从而补充了现有估计CATE的方法，这些方法通常会产生高维且不可解释的结果。我们提出一个目标函数，该函数在子组大小和效应大小之间进行权衡，调整控制此权衡的超参数会导致一个“前沿”上的帕累托最优规则集，其中没有任何一个规则集在所有标准上都优于其他规则集。通过样本分割可以实现有效的推断。我们使用模拟和实证例子来展示我们方法的实用性和局限性。 显示英文摘要

摘要： 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.