{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129990"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129990","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Empirical evaluation of constraint-based and score-based causal discovery algorithms","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Zifei Han, accepted the attached license on 2025-07-23 at 21:12.","The student, Zifei Han, submitted this Thesis for approval on 2025-07-23 at 21:22.","This Thesis was approved for publication on 2025-07-25 at 09:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22721 on 2025-10-20 at 20:15:45","This thesis provides guidance on choosing appropriate algorithms for causal discovery by conducting comparative experiments on constraint-based and score-based methods. Causal discovery is an essential field in studying cause and effect, and the specific causal discovery problem under consideration in this thesis focuses on revealing underlying causal relationships from purely observational data, with sufficient assumptions to achieve this. Specifically, several algorithm variants based on two classes of causal discovery methods are evaluated using simulated datasets, with these datasets varied in characteristics including linearity of the dominating functions, sample size, and noise level. Results show the difference in numerical outcomes of different algorithm variants from the aspects of accuracy and execution time, leading to conclusions about the expected performances under known dataset characteristics. These conclusions are of high guiding significance when applying these algorithms to real-world datasets."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Empirical evaluation of constraint-based and score-based causal discovery algorithms"]}]}],"canonical_facts":{"dc:contributor":["Kamalabadi, Farzad"],"dc:creator":["Han, Zifei"],"dc:date":["2025-07-25","2025-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Zifei Han, accepted the attached license on 2025-07-23 at 21:12.","The student, Zifei Han, submitted this Thesis for approval on 2025-07-23 at 21:22.","This Thesis was approved for publication on 2025-07-25 at 09:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22721 on 2025-10-20 at 20:15:45","This thesis provides guidance on choosing appropriate algorithms for causal discovery by conducting comparative experiments on constraint-based and score-based methods. Causal discovery is an essential field in studying cause and effect, and the specific causal discovery problem under consideration in this thesis focuses on revealing underlying causal relationships from purely observational data, with sufficient assumptions to achieve this. Specifically, several algorithm variants based on two classes of causal discovery methods are evaluated using simulated datasets, with these datasets varied in characteristics including linearity of the dominating functions, sample size, and noise level. Results show the difference in numerical outcomes of different algorithm variants from the aspects of accuracy and execution time, leading to conclusions about the expected performances under known dataset characteristics. These conclusions are of high guiding significance when applying these algorithms to real-world datasets."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129990"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Zifei Han"],"dc:subject":["Causal Discovery","Constraint-based Methods","Score-based Methods"],"dc:title":["Empirical evaluation of constraint-based and score-based causal discovery algorithms"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:06Z"}