30 citations · 42 across the 7 of their papers we have counts for
6 papers · 1 filter
A note on efficient minimum cost adjustment sets in causal graphical models
Ezequiel Smucler, Andrea Rotnitzky
We study the selection of adjustment sets for estimating the interventional mean under an individualized treatment rule. We assume a non-parametric causal graphical model with, pos…
Efficient adjustment sets for population average treatment effect estimation in non-parametric causal graphical models
Andrea Rotnitzky, Ezequiel Smucler
The method of covariate adjustment is often used for estimation of population average treatment effects in observational studies. Graphical rules for determining all valid covariat…
Characterization of parameters with a mixed bias property
Andrea Rotnitzky, Ezequiel Smucler, James M. Robins
In this article we study a class of parameters with the so-called `mixed bias property'. For parameters with this property, the bias of the semiparametric efficient one step estima…
A unifying approach for doubly-robust regularized estimation of causal contrasts
Ezequiel Smucler, Andrea Rotnitzky, James M. Robins
We consider inference about a scalar parameter under a non-parametric model based on a one-step estimator computed as a plug in estimator plus the empirical mean of an estimator of…
Consistency of Generalized Dynamic Principal Components in Dynamic Factor Models
Ezequiel Smucler
We study the theoretical properties of the generalized dynamic principal components introduced in Peña and Yohai (2016). In particular, we prove that when the data follows a dynami…
Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalised linear models
Efstathia Bura, Sabrina Duarte, Liliana Forzani +2
Reduced-rank regression is a dimensionality reduction method with many applications. The asymptotic theory for reduced rank estimators of parameter matrices in multivariate linear…