IPWerte
IPWerte, short for inverse probability weights, are statistical weights used to adjust for confounding and informative sampling in observational data. They are based on the estimated probability of receiving the observed treatment given observed covariates and aim to recreate a pseudo-population in which treatment assignment is independent of these covariates.
Calculation typically begins with estimating propensity scores, p(X) = P(T=1 | X), where T denotes treatment status and
IPWerte are commonly employed to estimate average treatment effects in causal inference, particularly within marginal structural
Practical considerations include assessing the distribution of weights, diagnosing balance after weighting, and checking overlap or
Historically, inverse probability weighting gained prominence in causal inference through the work of Robins, Hernán, Brumback,