Abstract: Causal mediation analysis examines the potential causal pathways between an exposure variable and outcome through intermediate variables with the goal of estimating direct and indirect effects. In practice, intermediate variables may be high-dimensional, in which case one may first aim to identify the true mediators among them. The dependence structure among mediators may then be studied with the goal of identifying a simple sufficient structure. We propose a two-stage penalized estimation procedure to meet these goals. The first stage involves selecting mediators by identifying nonzero indirect effects via a penalized regression. The second stage aims to simplify the correlation structure among selected mediators enabling the estimation of individual, grouped or joint effects. Through transformation of variables, the correlation selection problem can be reformulated as a standard LASSO problem. The two stages can be performed jointly or sequentially and we study the performance of each implementation through simulation studies. Finally, the proposed approach is applied to a psychiatry study in which the aim is to identify methylation loci that mediate the causal effect of childhood trauma on adult stress level.

Bio: 朱烨莹，现任加拿大滑铁卢大学统计和精算系副教授。研究领域包括因果分析，机器学习，中介分析等。论文曾发表在Journal of Machine Learning Research, Biometrika, Statistics in Medicine, Journal of Causal Inference, etc. 研究涉及多个跨学科领域：如将因果分析的方法用于社科和医学领域。 在工程方面的跨学科研究包括利用强化学习进行无人机的线路优化，运用机器学习方法进行时间序列的异常检测等。