8 papers
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…
Causal Inference with the Napkin Graph
Anna Guo, Lin Liu, David Benkeser +1
Unmeasured confounding can render identification strategies based on adjustment functionals invalid. We study the "Napkin" graph, a causal structure that encapsulates features of M…
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…
Order Dependence in Regression by Composition: Discussion on "Regression by Composition'' by Farewell, Daniel, Stensrud, and Huitfeldt
Mei Dong, Linbo Wang, Lin Liu +1
We discuss the regression-by-composition framework of Farewell, Daniel, Stensrud and Huitfeldt, highlighting a key consequence of its sequential construction: order dependence. Reo…
A Parameter-Centric View on Regression
Jingxin Yan, Lin Liu, Oliver Dukes +2
Discussion on ``Regression by Composition'' by Farewell, Daniel, Stensrud, and Huitfeldt
Toward Variation-Independent Regression by Composition
Ruixuan Zhao, Oliver Dukes, Linbo Wang +1
Discussion on "Regression by Composition" by Farewell, Daniel, Stensrud, and Huitfeldt.