A tutorial that offers practical guidance on the full analytic pipeline for causal inference using propensity score methods. These methods are especially useful for population-based studies using medical insurance claims dataset, which require thoughtful sample selection and analytic strategies to counter confounding, both measured and unmeasured. The methods are demonstrated for several common types of outcomes such as time to event and longitudinal repeated measures.