30 citations · 48 across the 13 of their papers we have counts for
12 papers · 1 filter
A note on the relation between one-step, outcome regression and IPW-type estimators of parameters with the mixed bias property
Andrea Rotnitzky, Ezequiel Smucler, James M. Robins
Bruns-Smith et al. (2025) established an algebraic identity between the one-step estimator and a specific outcome regression-type estimator for a class of parameters that forms a s…
A note on the properties of the confidence set for the local average treatment effect obtained by inverting the score test
Ezequiel Smucler, Ludovico Lanni, David Masip
We study the properties of the score confidence set for the local average treatment effect in non and semiparametric instrumental variable models. This confidence set is constructe…
On the asymptotic validity of confidence sets for linear functionals of solutions to integral equations
Ezequiel Smucler, James M. Robins, Andrea Rotnitzky
This paper examines the construction of confidence sets for parameters defined as linear functionals of a function of W and X whose conditional mean given Z and X equals the condit…
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 in causal graphical models with hidden variables
Ezequiel Smucler, Facundo Sapienza, Andrea Rotnitzky
We study the selection of covariate adjustment sets for estimating the value of point exposure dynamic policies, also known as dynamic treatment regimes, assuming a non-parametric…
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…
Asymptotic Statistical Properties of Redescending M-estimators in Linear Models with Increasing Dimension
Ezequiel Smucler
This paper deals with the asymptotic statistical properties of a class of redescending M-estimators in linear models with increasing dimension. This class is wide enough to include…
Robust and sparse estimators for linear regression models
Ezequiel Smucler, Víctor J. Yohai
Penalized regression estimators are a popular tool for the analysis of sparse and high-dimensional data sets. However, penalized regression estimators defined using an unbounded lo…