Marginal structural models is a class of statistical models developed to estimate the causal effect of a time-varying exposure or treatment on an outcome in the presence of time-varying confounding, where confounders may be affected by prior exposure and also influence future exposure or the outcome. This methodology employs inverse probability weighting techniques to create a pseudo-population where the exposure is independent of measured confounders over time, thereby enabling consistent estimation of marginal, population-level causal effects from observational longitudinal data.