Collaborative Causal and Bayesian Modeling
Collaborative data analysis projects using Bayesian and/or Causal methods.
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.
R Package for Dirichlet Process Mixtures of zero-inflated, logistic, and linear regressions.