1 code implementation • 6 Jan 2022 • Ezequiel Smucler, Andrea Rotnitzky
We show that in this setting there exist adjustment sets that are minimum cost optimal, in the sense that they yield non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that control for observable adjustment sets that have minimum cost.
1 code implementation • 22 Apr 2020 • Ezequiel Smucler, Facundo Sapienza, Andrea Rotnitzky
Moreover, we show that if either no variables are hidden or if all the observable variables are ancestors of either treatment, outcome, or the variables that are used to decide treatment, a globally optimal adjustment set exists.
no code implementations • 1 Dec 2019 • Andrea Rotnitzky, Ezequiel Smucler
In addition, for point interventions, we provide a sound and complete graphical criterion for determining when a non-parametric optimally adjusted estimator of an interventional mean, or of a contrast of interventional means, is as efficient as an efficient estimator of the same parameter that exploits the information in the conditional independencies encoded in the non-parametric causal graphical model.
no code implementations • 7 Apr 2019 • Ezequiel Smucler, Andrea Rotnitzky, James M. Robins
We focus on a class of parameters that have influence function which depends on two infinite dimensional nuisance functions and such that the bias of the one-step estimator of the parameter of interest is the expectation of the product of the estimation errors of the two nuisance functions.