Explain how to use propensity score stratification to address selection bias in observational studies.

Thank you for posing such a pertinent question, particularly in the realm of observational studies where selection bias can significantly skew our findings. Drawing from my extensive experience, especially in the capacity of a Data Scientist, I've leveraged propensity score stratification among other methods to ensure the integrity of causal inferences in several projects. At its core, propensity score stratification is a technique designed to reduce selection bias by equating groups based on covariates that predict receiving the treatment. The propensity score itself is the probability of each unit (like an individual in a study) receiving the treatment given their observed characteristics. To clarify, it's calculated using logistic regression, where the treatment assignment is regressed on observed covariates...