Statistical Methods Course Description
Overview: This workshop will cover the theory and implementation of propensity score methods for removing selection bias in analyses to estimate the effects of programs or conditions with non-experimental data. The multiple steps required for using propensity score methods will be detailed and implemented with real datasets from national surveys. Code and packages in the R statistical software for propensity score estimation, covariate balance evaluation, treatment effect estimation and sensitivity analysis will be demonstrated. The workshop will cover propensity score weighting, stratification and matching to estimate the average treatment effect and the average treatment effect on the treated.
Instructor: Walter L. Leite
is Associate Professor in the Research and Evaluation Methodology of College of Education at University of Florida. His research program consists of developing and evaluating statistical methods to strengthen causal inference and understanding of causal mechanisms using non-experimental data. He addresses obstacles to effective program evaluation with non-experimental data such as selection bias, measurement error, and attrition bias. He is the author of the book “Practical Propensity Score Methods Using R” published by Sage in 2016. Over the last 10 years, he has published 40 peer-reviewed articles and served as PI, co-PI, or program evaluator on 11 external grants, totaling over $18.9 million in funding.