Causal inference

因果推理

• 批准号: 10001302
• 负责人: Zhen Chen
• 金额: $ 0.88万
• 依托单位: EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10001302
• 关键词: Address; Air Pollution; Attention; Bayesian Modeling; Chemical Exposure; Chemicals; Data; Detection; Equation; Exposure to; Health; Household; Infertility; Literature; Markov chain Monte Carlo methodology; Mediating; Mediation; Mediator of activation protein; Methodology; Methods; Modeling; Morphology; Outcome; Pathway interactions; Performance; Seminal fluid; Structure; Uncertainty; cell motility; high dimensionality; interest; reproductive epidemiology; simulation

Mediation analysis aims at assessing the effect of some exposures on an outcome both through and around some mediators. While multi-mediator mediation analysis has been addressed in recent literature, the case with multiple exposures received little attention. With the presence of multiple exposure, we consider regularizations that allow variable selection and effects estimations simultaneously, hence stabilize model fit and account for model uncertainty. In the framework of linear structural equation models, we analytically show that a two-stage approach that regularizes on regression coefficients does not guarantee unimodal posterior distribution, and a product-in-coefficient approach that regularizes on direct and indirect effects tend to penalize excessively. We instead propose a regularized difference-in-coefficient approach that is free of these pitfalls. Using connections between frequentist regularizations and Bayesian hierarchical models with Laplace priors, we develop an efficient Markov chain Monte Carlo algorithm for posterior inference. Through simulations, we show that the proposed approach has good empirical performance in estimation. The methodology is illustrated using two data from reproductive epidemiology.

中介分析旨在评估某些暴露对通过某些中介或围绕某些中介的结果的影响。虽然最近的文献已经讨论了多中介中介分析，但多重暴露的案例却很少受到关注。在存在多重暴露的情况下，我们考虑允许同时进行变量选择和效应估计的正则化，从而稳定模型拟合并解释模型不确定性。在线性结构方程模型的框架中，我们分析表明，对回归系数进行正则化的两阶段方法不能保证单峰后验分布，而对直接和间接影响进行正则化的系数乘积方法往往会过度惩罚。相反，我们提出了一种没有这些陷阱的正则化系数差异方法。利用频率正则化和具有拉普拉斯先验的贝叶斯分层模型之间的联系，我们开发了一种用于后验推理的高效马尔可夫链蒙特卡罗算法。通过模拟，我们表明所提出的方法在估计方面具有良好的经验性能。使用生殖流行病学的两个数据说明了该方法。