ABSTRACT
Drawing causal inferences from observational data requires making strong assumptions about the causal process from which the data are generated, followed by a statistical analysis of the observational dataset. Though we must make causal assumptions, we often know little about the data-generating distribution. This means we generally cannot make strong statistical assumptions so we estimate a statistical parameter in a nonparametric or semiparametric statistical model. Semiparametric efficient estimators, that is, estimators that achieve the minimum asymptotic variance bound, such as augmented inverse probability of treatment weighted (A-IPTW) estimators [11] and targeted minimum loss-based estimators (TMLE) [15,18], have been developed for a variety of statistical parameters with applications in causal inference.
