2013/02/27 by Judea Pearl, Pearl, Judea · 2 citations
Computer Science · Mathematics · Medicine · #AI-based Problem Solving and Planning #Action (physics) #Artificial Intelligence (cs.AI) #Artificial intelligence #Bayes' theorem #Bayesian Modeling and Causal Inference #Bayesian network #Bayesian probability #Calculus (dental) #Chain rule (probability) #Computer science #Conditional probability #Conditioning #Constant (computer programming) #Discrete mathematics #FOS: Computer and information sciences #Graph #Law of total probability #Logic, Reasoning, and Knowledge #Mathematics #Medicine #Posterior probability #Probabilistic logic #Statistics #Theoretical computer science #cs.AI
paper · pdf · doi:10.48550/arxiv.1302.6835
published in arXiv (Cornell University) (Cornell University) · Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994)
arxiv created 2013/02/27 · openalex publication_date 2013/02/27 · arxiv updated 2013/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We present a symbolic machinery that admits both probabilistic and causal information about a given domain and produces probabilistic statements about the effect of actions and the impact of observations. The calculus admits two types of conditioning operators: ordinary Bayes conditioning, P(y|X = x), which represents the observation X = x, and causal conditioning, P(y|do(X = x)), read the probability of Y = y conditioned on holding X constant (at x) by deliberate action. Given a mixture of such observational and causal sentences, together with the topology of the causal graph, the calculus derives new conditional probabilities of both types, thus enabling one to quantify the effects of actions (and policies) from partially specified knowledge bases, such as Bayesian networks in which some conditional probabilities may not be available.