One of the core challenges of data science is drawing meaningful causal conclusions from observational data. In many such cases, the goal is to estimate the true impact of a treatment or behaviour as fairly as possible. This article explores Propensity Score Matching (PSM), a statistical technique used for that very purpose.
Unlike randomized experiments (A/B tests) or treatment trials, observatio...
This article presents Propensity Score Matching (PSM) as a robust tool for causal inference in observational data, emphasizing its utility in scenarios where randomized experiments are infeasible. The strongest version of this narrative highlights PSM's ability to reduce confounding bias by creating balanced comparison groups, as demonstrated through a clear, step-by-step application to e-commerce data. The analysis acknowledges limitations, such as reliance on observed covariates and the need f...