Establishing Causality Using Bayesian Networks

Establishing Causality Using Bayesian Networks

Presented at the 10th Annual BayesiaLab Conference on Tuesday, October 25, 2022.


A Bayesian Network is a popular framework for causal studies and for representing causal relationships among a network consisting of multiple variables. Causal relationships and their associated conditional probabilities can be represented in the structure of a Bayesian Network as nodes and edges, creating a Causal Bayesian Network. However, establishing causality extends beyond learning conditional probabilities from a dataset.

This presentation provides a crash course on the history of establishing causation in epidemiology, current viewpoints on defining causality, and a demonstration of how Bayesian Networks can be used to infer causation. We will examine the criteria for establishing a causal relationship, learning a Bayesian Network from a sample dataset, and augmenting (and improving) a Bayesian Network with informed prior knowledge from an ontology such as ICD-10.

Presentation Video

About the Presenter

Dr. Hengyi Hu is a data scientist and subject matter expert specializing in advanced analytics, performance analysis, process improvement, and program management. He has over 15 years of experience leading data-driven projects for the Department of Homeland Security, the National Science Foundation, and the Department of Justice. His current role in the Strategic Solutions Office at DHS HQ involves leading in-depth analysis of DHS-wide and government-wide category management spending, revamping specialized procurement reports and procurement reporting systems, and leading cross-agency collaborations for data-driven decision-making in category management.

Hengyi holds a B.S. degree in Information Sciences and Technology from Penn State University and M.S. and Ph.D. degrees in Information Technology from George Mason University. His research interests focus on causality, causal modeling, causal inference, and substantiating Bayesian networks learned from large datasets using causal mechanisms from authoritative ontologies. Hengyi holds certifications for PMP, CSSGB, FAC-COR II, and Strategy & Performance Management. Hengyi is also a graduate of the Key Executive Leadership Program at American University.

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