Empirical evaluation of constraint-based and score-based causal discovery algorithms
Han, Zifei
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https://hdl.handle.net/2142/129990
Description
Title
Empirical evaluation of constraint-based and score-based causal discovery algorithms
Author(s)
Han, Zifei
Issue Date
2025-07-25
Director of Research (if dissertation) or Advisor (if thesis)
Kamalabadi, Farzad
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Causal Discovery
Constraint-based Methods
Score-based Methods
Language
eng
Abstract
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.
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