25 citations · 25 across the 7 of their papers we have counts for
4 papers · 1 filter
Improved Variance Estimation in Homoskedastic Nonparametric Random-Design Regression via a Two-Scale Approach
Edgar Dobriban, Rajarshi Mukherjee, James M. Robins +1
We study estimation of a constant conditional variance in nonparametric regression with a -dimensional random design. This is an important problem, and similar questions a…
On the Asymptotic Inadmissibility of Double Machine Learning Estimators Under Structure-Agnostic Models
Lin Liu, Rajarshi Mukherjee, James M Robins
Structure-agnostic (SA) models introduced by Balakrishnan et al. (2026) aim to reflect the general lack of knowledge of structural assumptions on data-generating laws such as smoot…
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…
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…