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Toward Scalable and Valid Conditional Independence Testing with Spectral Representations
Alek Fröhlich, Vladimir R. Kostic, Karim Lounici +3
Conditional independence (CI) is central to causal inference, feature selection, and graphical modeling, yet it is untestable in many settings without additional assumptions. Exist…
Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression
Dimitri Meunier, Jakub Wornbard, Vladimir R. Kostic +5
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 us…
A Spectral-Grassmann Wasserstein metric for operator representations of dynamical systems
Thibaut Germain, Rémi Flamary, Vladimir R. Kostic +1
The geometry of dynamical systems estimated from trajectory data is a major challenge for machine learning applications. Koopman and transfer operators provide a linear representat…