Introduction | Simulated longitudinal observational data with right-censoring | Bayesian treatment effect weight estimation using bayesweight_cen | Bayesian non-parametric bootstrap to maximize the utility function with respect to the causal effect using bayesmsm | Visualization functions: plot_ATE, plot_APO, plot_est_box | Reference

Introduction | Simulated longitudinal observational data without right-censoring | Bayesian treatment effect weight estimation using bayesweight | Bayesian non-parametric bootstrap to maximize the utility function with respect to the causal effect using bayesmsm | Visualization functions: plot_ATE, plot_APO, plot_est_box | Reference

Introduction | Installation | Methods | Summary of the Unmeasured Confounder Problem | Simulated Data Mechanism | Frequentist Methods (Brumback et al. 2004, Li et al. 2011) | Bayesian Methods | Monte Carlo Approach to Causal Sensitivity Analysis