重新考虑反复暴露的因果推断:使用岩和岩的增量倾向得分方法
Wen Wei Loh1, Dongning Ren2, Yves Rosseel3
1Department of Methodology and Statistics, Faculty of Health, Medicine and Life Sciences (FHML), Maastricht University, Postbus 616, 6200 MD, Maastricht, The Netherlands. wenwei.loh@outlook.com.
Behavior research methods
|July 18, 2025
概括
评估经常性暴露的影响是很困难的. 增量倾向评分干预 (IPSI) 提供了一种现实的方法来评估暴露概率的变化如何影响结果,改善复杂场景的因果推断.
科学领域:
- 因果推理因果推理
- 流行病学 流行病学
- 心理学科学 心理学科学
背景情况:
- 评估经常性暴露 (例如,家庭暴力) 对行为和心理结果的因果关系带来了重大挑战.
- 传统方法通常依赖于不切实际的假设,并针对因果无关的数量,限制了实际应用.
- 现有的方法与过去的暴露预测未来的情况作斗争,阻碍了准确的估计.
研究的目的:
- 引入增量倾向得分干预 (IPSI) 作为一种用于因果推理的新方法,用于经常性暴露.
- 开发一个实用的估计程序IPSI使用lavaan,一个广泛采用的结构方程建模软件.
- 在对家庭暴力和青少年抑郁症的现实研究中展示IPSI的实用性.
主要方法:
- 这项研究引入了增量倾向得分干预 (IPSI) 框架.
- 开发了一个估计程序,使用lavaan进行结构方程建模.
- 该方法应用于一组数据,检查重复的家庭暴力及其对青少年抑郁症的影响.
主要成果:
- 增量倾向评分干预 (IPSI) 提供了一个更现实的方法,用于对反复暴露的因果推断.
- 开发的估计程序促进了IPSI在实证研究中的应用.
- 该研究证明了IPSI在理解复杂的暴露-结果关系方面的可行性和潜力.
结论:
- 与传统方法相比,增量倾向评分干预 (IPSI) 提供了一种更灵活和更有意义的方法,以得出关于反复暴露的因果结论.
- IPSI需要更少的假设,使其适用于更广泛的现实世界的场景.
- 这种新的方法提高了调查经常性暴露对行为和心理结果的因果关系的能力.
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