
Episode 16: Data Science and Decision Making Under Uncertainty
Vanishing Gradients
Understanding Causality and Probabilistic Thinking in Decision-Making
The chapter explores the significance of domain expertise in communicating decisions, focusing on causality and probabilistic thinking. Insights from Edwin James and Bayes are discussed, with examples like urn ball drawings and the Monty Hall problem to illustrate conditional probabilities. Emphasis is placed on explicit modeling, Bayesian analysis, and the importance of ensuring the superiority of complex models over simpler ones in decision-making processes.
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