Spectrally Deconfounded Gradient Boosting
Flexible machine-learning methods can be sensitive to hidden confounding: they may learn associations induced by unobserved confounders rather than stable signals. Spectral deconfounding mitigates this problem by shrinking high-variance directions of the covariate matrix that, under dense confounding, carry latent confounder information. Existing work has largely focused on linear models. We develop a nonlinear spectral deconfounding framework for gradient boosting. Our approach replaces the ordinary squared-error loss by a spectral loss, which alters the boosting dynamics by slowing down learning in confounding-aligned directions. We show that deconfounding is not achieved by the spectra...
| Authors | Andrea Nava, Peter Bühlmann, Fabio Sigrist |
|---|---|
| Primary category | stat.ML |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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