Jakub Wornbard — accepted AI-conference papers

2026

Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression

Dimitri Meunier, Jakub Wornbard, Vladimir Kostic, Antoine Moulin, Alek Fröhlich, Karim Lounici +2

ICML 2026poster

We address the problem of causal effect estimation in the presence of hidden confounders using nonparametric instrumental variable (IV) regression. An established approach is to use estimators based on learned \emph{spectral features}, that is, features spanning the top singular subspaces of the ope…

Cited by 0 Source ↗Scholar ↗

2025

Demystifying Spectral Feature Learning for Instrumental Variable Regression

Dimitri Meunier, Antoine Moulin, Jakub Wornbard, Vladimir R Kostic, Arthur Gretton

NeurIPS 2025poster

We address the problem of causal effect estimation in the presence of hidden confounders, using nonparametric instrumental variable (IV) regression. A leading strategy employs \emph{spectral features} - that is, learned features spanning the top eigensubspaces of the operator linking treatments to i…

Cited by 0 Source ↗Scholar ↗