Randomization is rarely available in observational or real-world data, which makes treatment groups fundamentally different at baseline. This tutorial walks through how to handle such confounding using multiple propensity score designs in R. Using the Lalonde dataset, we compare widely used approaches from MatchIt and WeightIt, including nearest neighbor matching, optimal matching, full matching, subclassification, entropy balancing, CBPS weighting, overlap weighting and more. Instead of promoting a single preferred method, the tutorial shows how each design alters covariate balance, sample structure and estimand, and demonstrates how outcome estimates change after applying these adjustments. The material is intended as a hands-on reference for analysts working in causal inference, health economics and outcomes research, and real-world evidence.

Randomization is rare in Real-World Evidence (RWE), so treatment groups are often biased at baseline. This tutorial shows how to adjust observational data using PSM (MatchIt) and IPTW (WeightIt), evaluate balance with cobalt, and run Cox and Kaplan–Meier survival analyses. Fully reproducible and designed for RWE workflows.

Have you ever tried to track something, but some outcomes were still unknown? That's what we call "censoring" in data. This simple guide explains how we still learn from those "unfinished stories" when analyzing how long things take to happen, making sense of real-world data where we don't always have all the answers.