标量函数因果发现用于用观察性可穿戴设备数据生成因果假设
Valeriya Rogovchenko1, Austin Sibu, Yang Ni
1Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 31, 2023
概括
这项研究引入了一种使用可穿戴设备数据进行因果推理的新方法. 它可以从单纯的观察数据中确定连续的功能健康数据和二进制健康结果之间的因果关系.
科学领域:
- 数字健康数字健康
- 因果推理因果推理
- 医疗健康数据分析
背景情况:
- 可穿戴设备为健康指标提供连续的高分辨率功能数据.
- 传统方法往往侧重于关联,限制因果机制的发现.
研究的目的:
- 开发一种新的方法,用于生成连续函数变量和二进制标量变量之间的因果假设.
- 超越以关联为中心的方法,揭示潜在的因果机制.
主要方法:
- 提出了一个可与观测数据识别的标量函数因果模型.
- 开发了一种基于原则的算法,比较竞争的因果假设的概率函数.
主要成果:
- 理论上证明了使用观察数据识别标量函数因果模型的可识别性.
- 通过模拟和现实数据验证了该方法的稳定性和适用性.
结论:
- 这种新方法可以从连续的功能和二进制标量健康数据生成因果假设.
- 该方法适用于观察数据,包括可穿戴设备数据,以揭示因果关系.
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