Propensity scores are popular for figuring out cause and effect in research. This article explains two main methods using propensity scores: matching and inverse probability weighting. These methods help researchers make decisions based on assumptions about the data. The article also talks about common mistakes to avoid when using these methods. Overall, the methods differ in how they use statistics and who they are meant for, but they all aim to estimate the effects of exposure on a specific group of people.