Inverse Probability Weighting for Time-Varying Treatments

Dr. Walter Leite demonstrates how to use propensity scores to remove selection bias for the estimation of the effect of a time-varying treatment in a longitudinal study. The estimation of inverse probability weights and stabilized inverse probability weights is demonstrated, and the average treatment effect (ATE) is estimated with weighted regression and generalized estimating equations. This video is based on Chapter 9 of the book Practical Propensity Score Methods Using R by Walter Leite. The code and data for this video can be found here: