University of Pittsburgh

Instance-Specific Causal Bayesian Network Structure Learning

Thursday, September 24, 2020 - 10:00am - 12:00pm


Much of science consists of discovering and modeling causal relationships in nature. Causal knowledge provides insight into the mechanisms acting currently (e.g., the side-effects caused by a new medication) and the prediction of outcomes that will follow when actions are taken (e.g., the chance that a disease will be cured if a particular medication is taken). In the past 30 years, there has been tremendous progress in developing computational methods for discovering causal knowledge from observational data. Some of the most significant progress in causal discovery research has occurred using causal Bayesian networks (CBNs). A CBN is a probabilistic graphical model that includes nodes and edges. Each node corresponds to a domain variable and each edge (or arc) is interpreted as a causal relationship between a parent node (a cause) and a child node (an effect), relative to the other nodes in the network.


In this dissertation, I focus on two problems: (1) developing efficient CBN structure learning methods that learn CBNs in the presence of latent variables (i.e., unmeasured or hidden variables). Handling latent variables is important in causal discovery since it can induce dependencies that need to be distinguished from direct causation. (2) developing instance-specific CBN structure learning algorithms to learn a CBN that is specific to an instance (e.g., patient), both with and without latent variables. Learning instance-specific CBNs is important in many areas of science, especially the biomedical domain; however, it is an under-studied research problem. In this dissertation, I develop various novel instance-specific CBN structure learning methods and evaluate them using simulated and real-world data.

Join Zoom Meeting:


Copyright 2009–2021 | Send feedback about this site