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The Book of Why
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Stefan Conrady
Managing Partner, Bayesia USA & Singapore
Given its counterintuitive (and controversial) solution, the Monty Hall problem has been debated extensively in academia and popular science. Judea Pearl revisits this paradox in The Book of Why. We now illustrate the solution with a Bayesian network in BayesiaLab.
Stefan Conrady
Managing Partner, Bayesia USA & Singapore
Unless you understand all the causal and noncausal paths in your problem domain, stay away from claiming "data-driven" policy recommendations. Judea Pearl's birth-weight paradox is an all-too-common problem, which we examine with BayesiaLab.
Stefan Conrady
Managing Partner, Bayesia USA & Singapore
How to block 14 noncausal paths in a causal Bayesian network? Try out the Smoking & Asthma example in BayesiaLab to see how easy it is to identify causal and noncausal paths.
Stefan Conrady
Managing Partner, Bayesia USA & Singapore
"Noncausal paths are the source of confounding." If this statement is not entirely clear, you should read The Book of Why and then experiment with the book's examples, which we implemented for you in BayesiaLab. So, try out these "games" and develop an intuition for the Back-Door Criterion.