Bayesian inference can make a difference in causal inference. These areas include learning from noisy experiments, generalization to new scenarios, meta-analysis, decision analysis, and models for measurement error. We discuss applications in biology, psychology, marketing, and other areas. These ideas should be relevant if you are interested in using Bayesian methods and also if you want to use these ideas to understand other statistical approaches.
http://www.stat.columbia.edu/~gelman/
This is a virtual presentation, please contact Tiffany at tdpence@wisc.edu for additional information.
