Randomized clinical trials are considered the goal standard for estimating causal effects. Nevertheless, in studies that are aimed at examining adverse effects of interventions these are often impractical because of ethical and financial considerations. In observational studies, matching on the generalized propensity scores was proposed as a possible solution to estimate the treatment effects of multiple interventions. However, the derivation of point and interval estimates for these matching procedures can become complex with non-continuous or censored outcomes. We propose novel approximate Bayesian bootstrap algorithms that result in statistically valid point and interval estimates of the treatment effects with dichotomous outcomes. The procedures rely on the estimated generalized propensity scores and multiply impute the unobserved potential outcomes for each unit. In addition, we describe a corresponding easily interpretable sensitivity analysis to examine the unconfoundedness assumption. The procedures are motivated and illustrated using an observational study that examines the cardiovascular safety of common, real-world anti-diabetic treatment regimens for Type 2 diabetes.