Posted on Monday, August 23, 2021
Guidelines for Using State-of-the-Art Methods to Estimate Propensity Score and Inverse Probability of Treatment Weights When Drawing Causal Inferences
Course Description: The estimation of causal effects is one of the primary activities of most longitudinal research studies. For example, analysts might want to understand whether a particular substance abuse treatment program is effective for its clients, whether school-based substance prevention actually reduces substance use, whether interventions can improve the quality and efficiency of mental health care, or whether incentives can increase military recruiting or the retention of service members. Controlled experiments are held as the gold standard for estimating such effects. However, experiments are often infeasible for many reasons and only observational data, in which participation in a program or intervention is out of the control of the researchers, are available for analysis.
This short course will provide an introduction to causal modeling using the potential outcomes framework and use of propensity score weights in the estimation of causal effects from observational data. The course will also provide step-by-step guidelines on how to estimate and perform diagnostic checks of propensity score weights for evaluations examining the relative effectiveness of two interventions. The course will also discuss methods for assessing the sensitivity of finding to unobserved covariates. Attendees will gain hands on experience estimating propensity score weights, evaluating the quality of those weights, and utilizing the weights for estimating intervention effects. The course can provide demonstrations of available software in R, SAS, Stata and Shiny (as needed) for fitting the models and opportunities for conducting analyses. The primary goals of the course are for attendees to have an understanding of how to implement propensity score weighting using state-of-art-methods and insights into some of the practical issues that evaluating the quality of propensity score weights involve.
