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Causal Inference

Determining cause-and-effect relationships from data, going beyond correlation to understand causal mechanisms.

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Evaluation A/B Testing Comparing two model versions in production by routing traffic to each and measuring performance differences. Foundations Bayesian Inference Using Bayes' theorem to update beliefs about parameters given data, incorporating uncertainty. Shipping AI Explainability The ability to explain how an AI model makes decisions in human-understandable terms, crucial for trust and accountability. Evaluation Data Leakage When information from outside the training data is used to create the model, leading to overly optimistic performance estimates.