Awesome, and well-deserved. For those who, like me, kinda know about Pearl's bayes nets but want to learn more, or operationalize their abstract knowledge, I highly recommend the Stanford Probabilistic Graphical Model course. It's still in its first week, so it's not too late to join.
(I posted this on the March 1-15 open thread, but I doubt many people saw it).
Link to ACM press release.
In addition to their impact on probabilistic reasoning, Bayesian networks completely changed the way causality is treated in the empirical sciences, which are based on experiment and observation. Pearl's work on causality is crucial to the understanding of both daily activity and scientific discovery. It has enabled scientists across many disciplines to articulate causal statements formally, combine them with data, and evaluate them rigorously. His 2000 book Causality: Models, Reasoning, and Inference is among the single most influential works in shaping the theory and practice of knowledge-based systems. His contributions to causal reasoning have had a major impact on the way causality is understood and measured in many scientific disciplines, most notably philosophy, psychology, statistics, econometrics, epidemiology and social science.
While that "major impact" still seems to me to be in the early stages of propagating through the various sciences, hopefully this award will inspire more people to study causality and Bayesian statistics in general.