CANSSI National Seminar Series – Linbo Wang, Jan. 28, 2021

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Seminar: 1:00-2:30pm EST
Student Session: 3:00-4:00 EST

The Promises of Multiple Outcomes

Unobserved confounding presents a major threat to the validity of causal inference from observational studies. In this paper, we introduce a novel framework that leverages the information in multiple parallel outcomes for identification and estimation of causal effects. Under a conditional independence structure among multiple parallel outcomes, we achieve nonparametric identification with at least three parallel outcomes. We further show that under a set of linear structural equation models, causal inference is possible with two parallel outcomes. We develop accompanying estimating procedures and evaluate their finite sample performance through simulation studies and a  data application  studying the causal effect of the tau protein level on various types of behavioural deficits.

Linbo Wang is an assistant professor in the Department of Statistical Sciences, University of Toronto. He is also affiliate assistant professor in the Department of Statistics, University of Washington, and a faculty affiliate at Vector Institute. Prior to these roles, he was a postdoc in Harvard T.H. Chan School of Public Health. He obtained his Ph.D. from the University of Washington. His research interest is centred around causality and its interaction with statistics and machine learning.

Student Session

The student session after the talk will allow students to ask Linbo questions about his research, the talk, the recommended paper or career opportunities. If you’re a student, make sure to register for this session.

Journal Club

This month’s paper is Evaluation of causal effects and local structure learning of causal networks by Zhi Geng, Yue Liu, Chunchen Liu, and Wang Miao. This is a recent paper introducing a new identification framework for causal inference. It is closely related to previous work in genetics, high-dimensional statistics and machine learning, so it might be of interest to a broad audience. There is a lot of room for new developments in this direction, and we hope the talk will generate some interesting discussions. Linbo particularly recommends reading the first three sections.

Geng Z., Liu Y., Liu C. C., and Miao W. (2019). Evaluation of causal effects and local structure learning of causal networks. Annual Review of Statistics and Its Application, 6:103–124. Available at:

Those who want more information can also look at Chapters 1, 2.1, 2.2, 3, 6.1-6.5 of the book Causal Inference: What If by Hernán MA, Robins JM (2020), published by Boca Raton: Chapman & Hall/CRC. This book is freely available here:

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