2026-09-24T17:36:28Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/129990 2025-10-25 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

application/pdf en text 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 Empirical evaluation of constraint-based and score-based causal discovery algorithms Han, Zifei 2025-07-25 Kamalabadi, Farzad Causal Discovery Constraint-based Methods Score-based Methods eng 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. 2025-08 Text https://hdl.handle.net/2142/129990 Copyright 2025 Zifei Han Electrical & Computer Eng Electrical & Computer Engr University of Illinois Urbana-Champaign M.S. Thesis