Here Equations (2) and (3) mean that U and X together account for all confounding in the observational data, but the observed covariates X alone are not enough. Moreover, we impose the overlap condition 0 < P (A = 1 | U, X, G = O) < 1, which is a standard assumption in causal inference literature. Because of the unmeasured confounders U , the observational **data** alone is not enough to identify the treatment effect parameter τ in eq. (1).