25 citations · 25 across the 7 of their papers we have counts for
8 papers
Difference-in-Differences for Heterogeneous Effects of General Treatment Strategies Accounting for Time-Varying Covariates
Oliver Dukes, Zach Shahn, James Robins +1
We introduce a class of marginal Structural Nested Mean Models (mSNMMs) that enable modeling time-varying effect modification by a subset of covariates required as adjustment varia…
Optimal Value Inference for Reinforcement Learning
Nan Lu, Ethan Lee, James M. Robins +2
We study offline inference for the optimal value in reinforcement learning under finite state and action spaces. Two new nuisances are derived as fixed points of a self-induced Bel…
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…
Debiased Ill-Posed Regression
AmirEmad Ghassami, James M. Robins, Andrea Rotnitzky
In various statistical settings, the goal is to estimate a function which is restricted by the statistical model only through a conditional moment restriction. Prominent examples i…
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…
De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers
Victor Chernozhukov, Whitney Newey, Rahul Singh
We provide adaptive inference methods, based on regularization, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expecta…