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Andrew Gelman - Bayesian Methods in Causal Inference and Decision Making
Consider the problem of A/B testing (that is, an experiment or observational study designed to estimate the effect of some exposure or treatment). The basic data analysis workflow is to start by comparing the average outcomes under the two groups, and then to estimate varying treatment effects and adjust for pre-treatment imbalances. The basic decision making workflow is to start by looking at statistical significance and then to consider cost-benefit tradeoffs. We consider several places where Bayesian inference enters this workflow: priors on the treatment effect and its variation, priors on adjustment factors, partial pooling across experiments, poststratification, and decision analysis. Many theoretical and practical research questions arise even in the apparently simple case of randomized experiments.

Mar 7, 2022 05:00 PM in Paris

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Andrew Gelman
Professor of Statistics @Département de Statistique et Département des Sciences Politiques, Columbia Université, New York
Andrew Gelman is a professor of statistics and political science at Columbia University. He has received the Outstanding Statistical Application award three times from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina), and Regression and Other Stories (with Jennifer Hill and Aki Vehtari).