Judea Pearl revolutionized artificial intelligence (and related fields) by establishing the computational and representational foundations for processing uncertainty. His innovations—most notably the invention of Bayesian networks, pioneering heuristic search methods, and the creation of a formal causal inference framework—fundamentally reshaped how AI systems reason about uncertain information and cause–effect relationships.
In statistics and machine learning, the Markov blanket for a vertex (graph theory) in a graphical model contains all the variables that shield the node from the rest of the network. This means that the Markov blanket of a node is the only knowledge needed to predict the behavior of that node and its children. The term was coined by Judea Pearl in 1988 - wikipedia 
See Bayesian Belief Regeneration, Bayesian Belief Update, Bayesian Inference, Bayesian Inquiry, Bayesian Learning Models of Pain
YOUTUBE -bIoxkx2gkA
Pearl discusses the influence of his inspirational high school teachers in Tel Aviv. acm
Ward via matrix
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08:28 PM
As I glanced through these slides I didn't see any mention of reasoning with counterfactuals. My aversion to these comes from resilience engineering. My appreciation comes from Judea Pearl as I try to appreciate his notion of a Markov Blanket. More on Pearl: https://amturing.acm.org/award_winners/pearl_2658896.cfm