Under the potential outcomes framework, there are three broad approaches for drawing inference on causal estimands - Neymanian, Fisherian and Bayesian. The Bayesian approach has grown in popularity due to its ability of including automatic uncertainty quantification, coherently incorporating prior knowledge, and offering a rich collection of advanced models for complex data. In this talk we will discuss the general structure of Bayesian causal inference, some recent advances in this area and a couple of applications.

Tirthankar Dasgupta is a Professor in the Department of Statistics, Rutgers University. His research interests include causal inference, experimental design, statistical applications in the physical sciences, engineering and biomedical sciences, and quality engineering. He received his Ph.D. in industrial engineering from Georgia Institute of Technology in 2007. Presently he serves on the Editorial Boards of the Journal of the American Statistical Association, Journal of the Royal Statistical Society (Series B) and Statistical Science.