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CRE:用于解释性发现和推断异质治疗效应的R包
Riccardo Cadei1,2, Naeem Khoshnevis3, Kwonsang Lee1
1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
Journal of open source software
|July 30, 2025
概括
在健康和社会科学中,确定具有异质治疗效应 (HTE) 的可解释子组至关重要. 因果规则组合 (CRE) 提供了一个强大的树组合方法,以克服单一树方法在HTE发现方面的局限性.
科学领域:
- 卫生和社会科学 卫生和社会科学
- 因果推理的原因推理.
- 机器学习 机器学习
背景情况:
- 识别具有异质治疗效应 (HTE) 的子组对于个性化医学和政策至关重要.
- 现有的方法,通常是以树为基础的,在子组识别稳定性和探索方面存在局限性.
- 单树方法在发现各种HTE模式时可能缺乏稳定性.
研究的目的:
- 引入因果规则组合 (CRE) 作为可解释的HTE特征的新方法.
- 通过采用树集方法来解决单一树方法的局限性.
- 提供一个灵活的R包实现CRE与各种内部估计器.
主要方法:
- 因果规则组合 (CRE) 使用树组合策略进行全面的HTE模式探索.
- 该方法侧重于可解释的决策规则来描述HTE.
- CRE包含多个内部个体平均治疗效果 (IATE) 估计器,包括AIPW,因果森林,因果BART,S-Learner,T-Learner和X-Learner.
主要成果:
- 与单一树方法相比,CRE增强了潜在异质性的稳定性和探索.
- 整体方法允许更全面地发现HTE模式.
- 该R套件提供了灵活性,可以使用不同的IATE估计技术来应用CRE.
结论:
- 因果规则集 (CRE) 提供了一个有效和可解释的解决方案,用于HTE发现.
- 该CRE R套餐促进了这种先进方法在健康和社会科学研究中的应用.
- 通过能够更强大地识别跨子组的治疗效果变化,CRE推进了因果推理领域.
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