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Judea Pearl's book Causality Models ,Reasoning and Inference starts with the Theory of Probability and explores the cause and effect Theories of science models. The Probability Theory combines a Predictive and a diagnostic approach , and we , Pathologists are applying just that everyday in our Professional life.Therefore ,I can tell the practical application of such theories using odds and likelihood ratio parameters. A good theory makes a good practice.The dictates of the approach is that the overall strength of a belief in any Hypothesis, is based on our previous experience /knowledge about it + the observed evidence (data collected and analysed as statistical proof). It is a product of 2 factors .
Review: Causality: Models, Reasoning, and InferenceUser Review - John Ledesma - Goodreads
A Note On “Causality: Models, Reasoning, and Inference” by Judea Pearl By Dr. Alex Liu August 2005 *** This is a note on my reading Judea Pearl's book “Causality: Models, Reasoning, and Inference ... Read full review
ACE(X actions algebraic algorithm analysis arrows Artificial Intelligence associated assumptions axioms back-door criterion back-door paths Bayesian networks calculus causal diagram causal effect causal inference causal model causal relationships Chapter coefficients compute concepts conditional independence conditional probability confounding consider correlation counterfactual covariance d-separated defined Definition dependent direct effect directed acyclic graph directed graph do(x econometrics equivalent estimate evaluation event example exogeneity experimental explanation expression factors Figure formal given graph graphical Greenland hypothetical identifiable implies instrumental variables interpretation intervention joint distribution Judea Pearl linear logic Markov Markovian mathematical measure mechanisms minimal nodes notion observed variables obtain P(yx parameters parents path coefficients Pearl potential-outcome predict probabilistic causality problem quantities query random represents Robins rules Science Section semantics set of variables Simpson's paradox specific Spirtes statistical structural equation models structural model subset sufficient Theorem theory tion treatment unobserved Yx(u
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