因果推理
杠杆(统计)
计算机科学
钥匙(锁)
数据科学
因果模型
推论
因果关系(物理学)
管理科学
风险分析(工程)
医学
机器学习
人工智能
计算机安全
物理
病理
量子力学
经济
作者
Koichiro Shiba,Kosuke Inoue
摘要
Abstract Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent integration of machine learning into causal inference has provided a new, flexible tool for evaluating complex HTEs: causal forest. Jawadekar et al. (Am J Epidemiol. 2023) introduce this innovative approach and offer practical guidelines for applied users. Building on their work, this commentary provides additional insights and guidance to promote the understanding and application of causal forest in epidemiologic research. We start with conceptual clarifications, differentiating between honesty and cross-fitting, and exploring the interpretation of estimated conditional average treatment effects. We then delve into the following practical considerations not addressed by Jawadekar et al., including motivations for estimating HTEs, calibration approaches, and ways to leverage causal forest output with examples from simulated data. We conclude by outlining challenges to consider for future advancements and applications of causal forest in epidemiological research.
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