Scaling-Up Bayesian Network Learning to Thousands of Variables Using Local Learning Techniques

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Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms (2019, background): “Many researches have been devoted to improving the performance of network construction [30], [32], [33] and probabilistic inference in causal graphs [34], [35].”
A Causal Framework for Discovering and Removing Direct and Indirect Discrimination (2016, background): “Many researches have been devoted to improving the performance of network construction (Kalisch and B¨uhlmann 2007; Tsamardinos et al. 2003; Aliferis et al. 2010) and probabilistic inference in causal networks (Heckerman and Breese 1994; 1996).”
The max-min hill-climbing Bayesian network structure learning algorithm (2006, methodology): “MMHC first learns the skeleton (i.e., the edges without their orientation) of a Bayesian network using a local discovery algorithm called Max-Min Parents and Children (MMPC), a first version of which (that did not incorporate the symmetry correction, see Section 3.3) was introduced in Tsamardinos, ”
Learning Bayesian networks: approaches and issues (2011, methodology): “Aliferis and Tsamardinos (2002) and Tsamardinos et al. (2003a, 2003b) focus on learning local features of a Bayesian network.”