Non-parametric Bayes

Bayesian modeling - flexiblilty, uncertainty quantification, full posterior inference.

Bayesian Causal Inference

Shrinkage, partial pooling, nonparametrics, and sensitivity analysis via priors - just some of the value Bayesian modeling can add to causal inference.

Applied Causal and Bayesian Modeling

Collaborative projects with interesting causal and Bayesian projects.


R Package for Dirichlet Process Mixtures of zero-inflated, logistic, and linear regressions.