SDModels

Spectrally Deconfounded Models (SDModels) is a package with methods to screen for and analyze non-linear sparse direct effects in the presence of unobserved confounding using the spectral deconfounding techniques (Ćevid, Bühlmann, and Meinshausen (2020), Guo, Ćevid, and Bühlmann (2022)). These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the SDModels will, in general, estimate a function closer to the true one than classical least squares optimization. SDModels provides software for Spectrally Deconfounded Additive Models (SDAMs) (Scheidegger, Guo, and Bühlmann (2025)) and Spectrally Deconfounded Random Forests (SDForest)(Ulmer, Scheidegger, and Bühlmann (2025)).