Co-Supervised Tree Synthesis for Interpretable Subgroup Identification and Honest Treatment Effect Inference in Randomized Trials
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共同监督树合成：随机试验中的可解释亚组识别与诚实处理效应推断
- 研究问题
- 如何在中等样本随机试验中兼顾处理效应异质性的预测准确性、亚组解释性，以及数据驱动选择后的有效推断？
- 方法与思路
- 作者以黑箱因果估计器标注合成数据，结合观测数据拟合单元效应模型，再按效应差异合成含稀疏线性叶模型的树。
- 材料陈述的贡献
- 摘要称该方法在所设模拟中缩小树与黑箱方法的精度差距，控制虚假分裂并达到名义覆盖率，且展示两个临床试验应用。
- 阅读关注点
- 应核对教师模型、合成数据生成及样本划分，尤其检查选择后叶效应推断有效的条件和模拟比较的适用范围。
- 阅读边界
- 这是arXiv预印本，同行评审及期刊接收未获验证。摘要未提供推断成立条件和具体覆盖率，所述准确性优势受效应形态限制。
原文摘要
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.