STA 290 Seminar: Peng Ding

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Event Date

Location
Mathematical Sciences Building 1147

Speaker: Peng Ding, Associate Professor, Statistics, UC Berkeley

Title: "Interpretable sensitivity analysis for the Baron-Kenny approach to mediation with unmeasured confounding"

Abstract: 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 (2020), we propose a sharp and interpretable sensitivity analysis method for the Baron-Kenny approach to mediation in the presence of unmeasured confounding. 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 sensitivity 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 version 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.

(Mingrui Zhang, Peng Ding; link to paper: https://arxiv.org/pdf/2205.08030.pdf)

Speaker Bio: Peng Ding is Associate Professor in the Department of Statistics at UC Berkeley. HIs research interests include causal inference in experiments and observational studies, with applications to biomedical and social sciences; contaminated data including missing data, measurement error, and selection bias. He received the Guy Medal in Bronze from the Royal Statistical Society. He serves on the editorial board of the Annals of Statistics, JASA and Biometrika.

Website: https://statistics.berkeley.edu/people/peng-ding

Seminar Time/Location: Thursday October 6, 4:10pm, Mathematical Sciences Building 1147

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