Post-Graduate Stories
Ruixuan Zhao Will Be Engaged in “Discovering Causal Patterns in Network-linked Data through Bayesian Networks”
As a 2025 CANSSI Ontario Postdoctoral Fellow in Statistical Sciences, Ruixuan Zhao will take part in a comprehensive program that involves teaching, interdisciplinary or applied collaboration, professional development, and a research project that aims to develop novel causal inference methodologies for network-linked data, addressing specific challenges in environmental health applications. She will work under the supervision of Professor Linbo Wang (University of Toronto) and Professor Guowen Huang (Western University).
Program: CANSSI Ontario Postdoctoral Fellowship in Statistical Sciences
Region: National
Date: 2025–2027
Project Focus Areas
Network-linked data, where units are interconnected and each possesses its own observed variables, have garnered substantial attention in recent years. For instance, in social networks, relationships between individuals generate dependencies arising from shared preferences and frequent interactions. Such dependencies challenge the common assumption of data independence in the causal inference literature, and neglecting them may lead to biased conclusions about causal effects. This project aims to utilize Bayesian networks, or directed acyclic graphs, to examine both the causal relationships within individual units and the cross-unit effects throughout the network.
Ruixuan Zhao’s work will focus on developing advanced statistical methodologies for analyzing environmental health data, specifically designed to address dependencies commonly encountered in real-world datasets. She will collaborate closely with the Centre for Global Health Research (CGHR) and will gain access to extensive datasets on health outcomes through the Open Mortality project.
During her fellowship, Ruixuan will participate in the Emergent Data Science Program led by Professor Wang at the University of Toronto and will deepen her understanding of environmental health data applications through collaborations with scientists at the Centre for Global Health Research. She will also have opportunities to teach and/or mentor students at both the University of Toronto and Western University and to present at workshops and conferences.
Finally, Ruixuan will receive training in grant writing, scientific communication, teaching, and EDI practices and will have access to interdisciplinary networks.
Getting to Know Ruixuan
Ruixuan Zhao obtained her PhD in the Department of Data Science at the City University of Hong Kong in 2024.
While conducting her doctoral research, she realized that she was “committed to advancing the frontiers of causal inference and facilitating the application of theoretical insights to real-world challenges.” Her PhD work focused on establishing identifiability conditions and developing learning methods for causal graphical models, such as directed acyclic graphical models and chain graph models. She conducted both theoretical and numerical analyses of learning methods to reconstruct exact causal graphs from observed data. Her research has been published in top-tier journals in the field of statistics and presented at several academic conferences.
Ruixuan gained valuable teaching experience as a teaching assistant during her PhD studies and looks forward to further enhancing her teaching skills through the CANSSI Ontario Postdoctoral Fellowship in Statistical Sciences program.
Ultimately, she intends to pursue a faculty position and believes that her postdoctoral experience will prepare her well by providing opportunities to work on “high-quality scientific research projects” and collaborate with “outstanding research partners.”
Read more about Ruixuan and her award on the CANSSI Ontario website.
I am committed to advancing the frontiers of causal inference and facilitating the application of theoretical insights to real-world challenges.
About the Supervisors
Linbo Wang
Linbo Wang is an Associate Professor and Canada Research Chair in Causal Machine Learning in the Department of Statistical Sciences at the University of Toronto. He received his PhD in Biostatistics from the University of Washington in 2016 and spent two years in the Harvard Causal Inference Program before coming to Toronto.
Linbo is a co-organizer of the Emerging Data Science Program, which brings together data science and causal inference for better policy recommendations, at the Data Sciences Institute, University of Toronto. He is the recipient of several research awards, including an NSERC Discovery Accelerator Supplement in 2019.
His research interests include causal inference, graphical models, and modern statistical inference in infinite-dimensional models.
He is currently interested in
- Causal inference with unmeasured confounding
- Variable selection in causal inference
- Parameterization of discrete graphical models
- Causal inference and optimal transport
To read more, see Linbo’s website.
Guowen Huang
Dr. Guowen Huang is an Assistant Professor in Statistics at Western University. He earned his PhD in Statistics from the University of Glasgow in 2016 under the supervision of Professors Duncan Lee and Marian Scott and previously held postdoctoral research positions at National Tsing Hua University and the University of Toronto. Before joining Western, Dr. Huang was an Associate Professor at Shantou University. His research specializes in spatial statistics, particularly the statistical modelling of air pollution data and its effects on human health.
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