Spectrally Deconfounded Models
Cross-validation for the SDTree
Function of x on a fourier basis
Get the sequence of complexity parameters of an SDForest
Get the sequence of complexity parameters of an SDTree
Estimation of spectral transformation
Estimation of anchor transformation
Merge two forests
Partial dependence
Plot partial dependence
Visualize the paths of an SDTree or SDForest
Plot performance of SDForest against number of trees
Plot SDTree
Visualize the out-of-bag performance of an SDForest
Predictions of individual component functions for SDAM
Predictions for SDAM
Predictions for the SDForest
Predictions for the SDTree
Out-of-bag predictions for the SDForest
Print partDependence
Print SDAM
Print SDForest
Prune an SDForest
Prune an SDTree
Calculate the regularization path of an SDForest
Calculate the regularization path of an SDTree
Spectrally Deconfounded Additive Models
Spectrally Deconfounded Random Forests
Spectrally Deconfounded Tree
Simulate data with linear confounding and non-linear causal effect
Simulate data with linear confounding and causal effect following a st...
Calculate the stability selection of an SDForest
Extract Variable importance for SDAM
Extract variable importance of an SDForest
Extract variable importance of an SDTree
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:10.1080/10618600.2025.2569602>).