Propensity Score Matching in R: A Practical Guide with Real-World Case Studies

Introduction In the real world, conducting randomized controlled experiments is not always feasible. Businesses cannot randomly assign customers to loyalty programs without operational constraints. Healthcare researchers cannot ethically assign harmful exposures to patients. Policymakers cannot randomly allocate economic reforms across cities just for experimentation. This is where Propensity Score Matching (PSM) becomes a powerful statistical solution. Propensity Score Matching is a method used in observational studies to estimate causal effects when random assignment is not possible. It helps reduce selection bias by matching treated and control units with similar characteristics, creating a quasi-experimental design from non-experimental data. In this article, we will explore: The origins of Propensity Score Matching How PSM works in simple terms Implementation of PSM in R Real-world applications across industries Case studies in marketing and healthcare Origins of Propensity Score