5 papers · 1 filter
Stabilized Higher-Order Influence Functions: Statistical Theory of a Class of Bilinear Forms
Na Liu, Chang Li, Yujia Gu +1
Higher-order influence functions, introduced in a series of articles (Robins et al., 2008, 2009a; van der Vaart, 2014; Robins et al., 2016, 2023; Liu et al., 2017), are a unified f…
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…
Semiparametric Efficient Empirical Higher Order Influence Function Estimators
Lin Liu, Rajarshi Mukherjee, Whitney K. Newey +1
Robins et al. (2008, 2017) applied the theory of higher order influence functions (HOIFs) to derive an estimator of the mean of an outcome Y in a missing data model with Y mis…
Method-of-Moments Inference for GLMs and Doubly Robust Functionals under Proportional Asymptotics
Xingyu Chen, Lin Liu, Rajarshi Mukherjee
In this paper, we consider the estimation of regression coefficients and signal-to-noise (SNR) ratio in high-dimensional Generalized Linear Models (GLMs), and explore their implica…
Root-n consistent semiparametric learning with high-dimensional nuisance functions under minimal sparsity
Lin Liu, Xinbo Wang, Yuhao Wang
Treatment effect estimation under unconfoundedness is a fundamental task in causal inference. In response to the challenge of analyzing high-dimensional datasets collected in subst…