不完全依从下基于树方法的异质性因果效应估计
Estimating Heterogeneous Causal Effects with Tree-Based Methods under Imperfect Compliance
- University of Duisburg-Essen（杜伊斯堡-埃森大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对不完全依从场景，提出两种非贝叶斯树方法（DRRF-IV和GRF-IV）估计条件依从者平均因果效应，模拟显示性能与BCF-IV相当且计算更快，并应用于ICU及时入院对死亡率影响的子组分析。
AI中文摘要：
我们研究了在不完全依从条件下，条件依从者平均因果效应（CCACE）估计方法对子组发现和异质性因果效应估计性能的影响。基于贝叶斯因果森林与工具变量（BCF-IV）方法（Bargagli-Stoffi等，2022），我们提出了一种两步、模型无关的方法，允许在第一步中使用任何合适的机器学习方法进行CCACE估计。具体而言，我们实现了两种非贝叶斯基于树的方法，均使用基于森林的学习器：DRRF-IV，一种去偏的转换结果回归森林方法，以及基于广义随机森林框架的IV适应的GRF方法（Athey等，2019）。通过模拟研究，我们评估了所提方法相对于BCF-IV在精度、偏差以及正确识别潜在子组结构方面的能力。结果表明，非贝叶斯方法在所考虑的模拟设置中表现具有竞争力，在较大样本量和中等偏大的处理效应下性能提升，同时减少了计算运行时间。我们通过重新审视一项实证研究来应用我们的新方法，该研究考察了英国48家国家卫生服务医院中及时入住重症监护病房（ICU）对28天死亡率的影响。虽然先前的工作发现没有显著的总体处理效应，但我们调查了是否某些患者子组可能从及时ICU入院中获益更多。
英文摘要：
We study the impact of conditional complier average causal effect (CCACE) estimation methods on the performance of subgroup discovery and heterogeneous causal effect estimation under imperfect compliance. Building on the Bayesian Causal Forest with Instrumental Variable (BCF-IV) (Bargagli-Stoffi et al. (2022)) method, we introduce a two-step, model-agnostic approach that allows any suitable machine learning method to be used for the CCACE estimation in the first step. Specifically, we implement two non-Bayesian tree-based methods, both using forest-based learners: DRRF-IV, a debiased transformed-outcome regression-forest approach, and a GRF-based IV adaptation of the generalized random forest framework (Athey et al., 2019). Through a simulation study, we assess the precision, bias, and the ability to correctly identify the underlying subgroup structure of the proposed methods relative to BCF-IV. The results show that the non-Bayesian methods perform competitively across the considered simulation settings, with performance improving for larger sample sizes and moderately large treatment effects, while reducing computational runtime. We apply our new methods by revisiting an empirical study that examines the effect of prompt admission to intensive care units (ICU) on 28-day mortality across 48 UK National Health Service hospitals. While previous work finds no significant overall treatment effect, we investigate whether subgroups of patients may benefit more from prompt ICU admission.
