CAUSALRLSTACK:深度表示和因果效应估计的自适应平衡,适用于与艾滋病毒有关的健康数据
Dat Thanh Pham1, Khai Quang Tran2, Viet Anh Nguyen3
1Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hoang Quoc Viet, Ha Noi, 100000, Viet Nam. datpt.ncs@ioit.ac.vn.
BioData mining
|November 5, 2025
概括
本研究介绍了CAUSALRLSTACK,这是一个灵活的框架,用于估计健康数据中的个性化因果影响. 该模型在复杂的健康分析中表现出卓越的性能,增强了个性化干预策略.
科学领域:
- 因果推理因果推理
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 估计个性化因果关系对于在公共卫生中做出数据驱动的决策至关重要.
- 现有的因果推理模型由于集成的表示学习和效果估计而难以灵活和通用.
- 复杂的健康数据需要先进的分析方法来准确分析因果关系.
研究的目的:
- 开发一个模块化和适应性框架,用于在复杂的健康数据中增强个性化因果关系分析.
- 提高因果推理模型的灵活性和通用性.
- 为公共卫生领域的个性化干预策略提供实际基础.
主要方法:
- 提出 CAUSALRLSTACK,一个分离表示学习 (增强记忆的变压器 - TITAN) 和因果效应估计 (双重可靠的估计器 - DRLearner) 的模块化框架.
- 采用强化学习代理,例如特定的适应权重,增强跨人群的概括性.
- 使用因果图,在专家定义的和数据发现的选项中进行选择,用于输入特征导出.
主要成果:
- 在两个HIV数据集上,CAUSALRLSTACK的表现优于六个最先进的模型.
- 获得了最高的准确性 (0.861,0.855),F1-Score (0.845,0.839) 和AUC-ROC (0.897,0.892) 的结果.
- 在预测不确定性最低 (0.093,0.092) 的情况下表现出强的性能.
结论:
- 拟议的框架为个性化因果推理提供了一个灵活,适应性和有效的解决方案.
- 基于强化学习的权重使得在不同人群中能够进行数据驱动的估计.
- 该框架有可能在健康数据中推进个性化因果推理,并为个性化公共卫生干预提供信息.
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