协同监督树合成：用于随机试验中可解释亚组识别与诚实治疗效果推断
Co-Supervised Tree Synthesis for Interpretable Subgroup Identification and Honest Treatment Effect Inference in Randomized Trials
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- The Pennsylvania State University（宾夕法尼亚州立大学）
- Florida State University（佛罗里达州立大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
提出CausalSynthTree，用黑盒因果估计器指导可解释树构建，实现亚组识别与诚实推断，在模拟和真实试验中优于现有方法。
中文摘要 AI 辅助
识别具有异质性治疗效果的病人亚组是精准医学的核心，然而现有方法面临一种张力。黑盒方法能很好地估计个体化效果，但不产生可解释的亚组，而基于树的方法产生明确的划分，但在中等样本量的试验中不稳定且准确性较低。我们提出CausalSynthTree，一种协同监督方法，其中黑盒因果估计器指导可解释树的构建。它将协变量空间划分为单元，从观测数据和由黑盒因果教师标记的合成数据中拟合单元级条件平均治疗效果模型，并通过治疗效果差异准则将它们合成为一棵树。每个叶节点携带一个稀疏线性模型，因此树路径定义了一个亚组，叶模型显示哪些协变量修饰效果。尽管有协同监督增强和数据驱动的亚组选择，叶级效果的诚实推断仍然有效。在模拟中，它缩小了两个家族之间的很大差距。从中等样本量开始，当效果是线性的或具有单一阈值时，它比黑盒学习器更准确，并且仅在比它生成的树更细的划分上落后于它们。它控制了虚假分裂并始终达到名义覆盖率。在ACTG175 HIV试验中，标准交互检验未检测到效果修饰，该方法报告无亚组，而竞争树方法报告了几个。在辅助结肠癌试验中，它在部分重采样中报告了一个临界协变量，以及一个与独立检验一致的区域内年龄梯度。
英文摘要
Identifying patient subgroups with heterogeneous treatment effects is central to precision medicine, yet existing approaches face a tension. Black-box methods estimate individualized effects well but yield no interpretable subgroups, while tree-based methods produce explicit partitions but are unstable and less accurate in moderate-sample trials. We propose CausalSynthTree, a co-supervised method in which black-box causal estimators guide construction of an interpretable tree. It partitions the covariate space into cells, fits cell-wise conditional average treatment effect models from both observed data and synthetic data labeled by black-box causal teachers, and synthesizes them into a tree through a treatment-effect disparity criterion. Each leaf carries a sparse linear model, so the tree path defines a subgroup and the leaf model shows which covariates modify the effect. Honest inference for leaf-wise effects remains valid despite co-supervised augmentation and data-driven subgroup selection. In simulations it closes much of the gap between the two families. From moderate sample sizes onward it is more accurate than the black-box learners whenever the effect is linear or has a single threshold, and trails them only on a finer partition than the tree it grows. It controls spurious splits and attains nominal coverage throughout. In the ACTG175 HIV trial, where standard interaction tests detect no effect modification, the method reports no subgroups while the competing tree methods report several. In an adjuvant colon cancer trial it reports a borderline covariate in part of the resamples, together with a within-region age gradient that is consistent with an independent test.
