Noah Greifer
Noah Greifer, Ph.D., is a statistical consultant and programmer at the Institute for Quantitative Social Science at Harvard University.
Greifer’s focus is on the development, application, and dissemination of statistical methods for causal inference. He has applied his expertise to substantive research in medicine, public health, psychology, criminology, education, and public policy.
Greifer received his Ph.D. in Quantitative Psychology from the University of North Carolina at Chapel Hill. His research has focused on parametric modeling-, optimization-, and machine learning-based methods for matching and weighting in observational studies. He is the author and maintainer of several R packages for causal and statistical inference, including MatchIt for matching methods, WeightIt for weighting methods, and cobalt for assessing balance in observational studies, among many others.
His research has appeared in several journals related to statistics and statistical computing, including Journal of Statistical Software, R Journal, Journal of the American Statistical Association, and Observational Studies. His R packages are known for their extensive documentation, user-friendliness, and broad functionality and are widely used across substantive fields.
You can visit his personal webpage here.
You can visit is GitHub page here.