varImp.SDTree

This function extracts the variable importance of an SDTree. 
The variable importance is calculated as the sum of the decrease in the loss 
function resulting from all splits that use this covariate.

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>).

Markus Ulmer

SDModels

Spectrally Deconfounded Models

Cyrill Scheidegger

varImp.SDTree function

<dl><dt>object</dt>
<dd>an SDTree object</dd></dl>

Arguments

Author

Extract variable importance of an SDTree — varImp.SDTree

<dl>

<dt>object</dt>
<dd>an SDTree object</dd>

</dl>

varImp.SDTree: Extract variable importance of an SDTree

Description

Usage

Value

Arguments

Author

See Also

Examples