Inverse probability weighting

Inverse probability weighting reweights each observed outcome by the reciprocal probability of receiving its observed treatment, creating a weighted population in which treatment is balanced across measured covariates.

For binary treatment with propensity score $\pi (X)$, the overlap weight $\pi (X)\{1-\pi (X)\}$ emphasizes covariate strata in which both treatments occur with appreciable probability.