Assistant professor of health care policy Jose Zubizarreta, PhD, gave an invited course titled New Matching for Causal Inference and Impact Evaluation at the 2018 International Meeting of the Psychometric Society.

Zubizarreta’s course explored new advancements in matching methods that overcome three limitations of standard matching approaches. These advancements allow investigators to (1) directly obtain flexible forms of covariate balance, ranging from mean balance to balance of entire joint distributions, (2) produce self-weighting matched samples that are representative of target populations by design, and (3) handle multiple treatment doses without resorting to a generalization of the propensity score, instead balancing the original covariates. Zubizarreta also discussed extensions to matching with instrumental variables and in discontinuity designs, and for matching before randomization in experiments. The course utilized his statistical software package 'designmatch' for R.

The Psychometric Society was founded in 1935 to advance quantitative methodology in the behavioral sciences. The International Meeting of the Psychometric Society is held annually, with Columbia University in New York City hosting the 2018 meeting.