SDModels: Spectrally Deconfounded Models

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)<jmlr.org/papers/v21/19-545.html>, Guo, Ćevid, and Bühlmann (2022) <doi:10.1214/21-AOS2152>). 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 deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. 'SDModels' provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) <doi:10.1145/3711116>) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) <doi:10.48550/arXiv.2502.03969>).

Package details

AuthorMarkus Ulmer [aut, cre, cph] (<https://orcid.org/0000-0001-7783-8475>), Cyrill Scheidegger [aut] (<https://orcid.org/0009-0005-2851-1384>)
MaintainerMarkus Ulmer <markus.ulmer@stat.math.ethz.ch>
LicenseGPL-3
Version1.0.7
URL https://www.markus-ulmer.ch/SDModels/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("SDModels")

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SDModels documentation built on April 11, 2025, 5:50 p.m.