Empirical evaluation of constraint-based and score-based causal discovery algorithms

Han, Zifei

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https://hdl.handle.net/2142/129990 Copy

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

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.

Graduation Semester

2025-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129990

Copyright and License Information

Copyright 2025 Zifei Han

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