Probabilistic Graphical Models: Principles and Techniques, Daphne Koller, Nir Friedman, 2009 (The MIT Press) - The definitive textbook for probabilistic graphical models, with comprehensive chapters dedicated to structure learning in Bayesian Networks, covering algorithms, scoring functions, and theoretical foundations.
Causality, Judea Pearl, 2009 (Cambridge University Press)DOI: 10.1017/CBO9780511803161 - A foundational book discussing the theory of causal inference through graphical models, relevant for understanding the principles behind constraint-based structure learning and Markov equivalence.
Causation, Prediction, and Search, Peter Spirtes, Clark N. Glymour, Richard Scheines, 2000 (The MIT Press)DOI: 10.7551/mitpress/1754.001.0001 - A seminal work introducing the PC algorithm and the theoretical underpinnings of constraint-based causal discovery, a major approach to Bayesian Network structure learning.