收缩贝叶斯因果森林与工具变量
Shrinkage Bayesian Causal Forest with Instrumental Variable
- University of Duisburg-Essen（杜伊斯堡-埃森大学）
- Faculty of Business Administration and Economics（经济与商业学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对稀疏高维工具变量分析，提出收缩贝叶斯因果森林SBCF-IV，通过稀疏先验和CART成本引导发现异质性依从者效应亚组，实验证明优于BCF-IV。
AI中文摘要：
在不完全依从的工具变量分析中，发现其依从者效应偏离平均值的可解释亚组是一个核心目标，然而当大多数协变量与效应无关时，现有的基于树的方法性能会下降。我们提出了带有工具变量的收缩贝叶斯因果森林（SBCF-IV），用于在稀疏高维设置中发现和估计具有异质性依从者平均因果效应（CACE）的亚组。SBCF-IV在估计条件意向治疗效应和依从者比例的贝叶斯加性回归树上，对分裂概率施加了诱导稀疏性的狄利克雷先验，将后验质量集中在少数调节依从者效应的协变量上，从而正则化效应估计。后验分裂频率还作为变量级成本进入下游的CART，引导划分朝向相关的调节变量，提供协变量空间的可解释划分。蒙特卡洛实验表明，随着无关协变量比例的增加，SBCF-IV在树和单元水平上比其非稀疏前身BCF-IV更可靠地恢复真实划分，并在BCF-IV的区间恶化时保持名义覆盖率。我们将该方法应用于俄勒冈健康保险实验和401(k)资格数据。
英文摘要：
Discovering interpretable subgroups whose complier effects deviate from the average is a central goal of instrumental variable analysis under imperfect compliance, yet existing tree-based methods degrade when most covariates are irrelevant to the effect. We propose Shrinkage Bayesian Causal Forest with Instrumental Variable (SBCF-IV) for discovering and estimating subgroups with heterogeneous Complier Average Causal Effects (CACE) in sparse high-dimensional settings. SBCF-IV places a sparsity-inducing Dirichlet prior on the splitting probabilities of the Bayesian Additive Regression Trees that estimate the conditional intention-to-treat and the complier share, concentrating posterior mass on the few covariates that moderate the complier effect and thereby regularizing effect estimation. The posterior split frequencies additionally enter a downstream CART as variable-level costs that steer the partition toward relevant moderators, providing an interpretable division of the covariate space. Monte Carlo experiments show that, as the share of irrelevant covariates grows, SBCF-IV recovers the true partition more reliably than its non-sparse predecessor BCF-IV at the tree and unit level, and retains nominal coverage where BCF-IV's intervals deteriorate. We apply the method to the Oregon Health Insurance Experiment and the 401(k) eligibility data.
