Causal Structure Learning by Using Intersection of Markov Blankets
This addresses the problem of causal inference for researchers in machine learning and statistics, but appears incremental as it builds on existing methods like the PC algorithm.
The paper tackles causal structure learning by introducing the EEMBI algorithm, which combines Bayesian networks and Structural Causal Models, and an extended version EEMBI-PC that integrates the PC algorithm, resulting in a novel method for this task.
In this paper, we introduce a novel causal structure learning algorithm called Endogenous and Exogenous Markov Blankets Intersection (EEMBI), which combines the properties of Bayesian networks and Structural Causal Models (SCM). Furthermore, we propose an extended version of EEMBI, namely EEMBI-PC, which integrates the last step of the PC algorithm into EEMBI.