学术空间

数学与统计及交叉学科前沿论坛------高端学术讲座第83场

讲座题目:Interpretable sensitivity analysis for the Baron-Kenny approach to mediation with unmeasured confounding

主讲人:丁鹏  副教授   加州大学伯克利分校

讲座时间:2023531日下午14:00-15:00

讲座地点:良乡校区数统楼311


主讲人简介:

  丁鹏,加州大学伯克利分校统计系副教授,于20155月获得了哈佛大学统计系博士学位,并于201512月在哈佛大学公共卫生学院流行病学系做博士后。在此之前,丁鹏教授在北京大学获得了数学学士、经济学学士和统计学硕士学位。


主讲内容:

       Mediation analysis assesses the extent to which the treatment affects the outcome indirectly through a mediator and the extent to which it operates directly through other pathways. As the most popular method in empirical mediation analysis, the Baron-Kenny approach estimates the indirect and direct effects of the treatment on the outcome based on linear structural equation models. However, when the treatment and the mediator are not randomized, the estimates may be biased due to unmeasured confounding among the treatment, mediator, and outcome. Building on Cinelli and Hazlett (2020a), we propose a sharp and interpretable sensitivity anal-ysis method for the Baron-Kenny approach to mediation in the presence of unmeasured con- founding. We first modify their omitted-variable bias formula to facilitate the discussion with heteroskedasticity and model misspecification. We then apply the result to develop a sensitive-ity analysis method for the Baron-Kenny approach. To ensure interpretability, we express the sensitivity parameters in terms of the partial R2’s that correspond to the natural factorization of the joint distribution of the direct acyclic graph for mediation analysis. They measure the proportions of variability explained by unmeasured confounding given the observed variables. Moreover, we extend the method to deal with multiple mediators, based on a novel matrix ver-sion of the partial R2 and a general form of the omitted-variable bias formula. Importantly, we prove that all our sensitivity bounds are attainable and thus sharp.