A comparison between Bayesian and heuristic machine-learning models that learn probability information from experience under uncertainty.
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Updated
Nov 26, 2024 - PostScript
A comparison between Bayesian and heuristic machine-learning models that learn probability information from experience under uncertainty.
Production-style A/B testing with binomial GLMs (logit/probit): covariate adjustment, marginal ATE/risks, cluster-robust SEs, and Brier-score calibration.
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