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Instance-Specific Causal Bayesian Network Structure Learning

Jabbari, Fattaneh (2021) Instance-Specific Causal Bayesian Network Structure Learning. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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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.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Jabbari, Fattanehfaj5@pitt.edufaj5
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairCooper, Gregory
Committee MemberLu,
Committee MemberVisweswaran,
Committee MemberSpirtes,
Date: 20 January 2021
Date Type: Publication
Defense Date: 24 September 2020
Approval Date: 20 January 2021
Submission Date: 28 October 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 218
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Causal Bayesian network structure learning, Instance-specific modeling, Population-wide modeling, Causal inference
Date Deposited: 20 Jan 2021 18:48
Last Modified: 20 Jan 2021 18:48

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  • Instance-Specific Causal Bayesian Network Structure Learning. (deposited 20 Jan 2021 18:48) [Currently Displayed]


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