plot.SDForest

This plot helps to analyze whether enough trees were used. If the loss does
not stabilize one can fit another SDForest and merge the two.

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

plot.SDForest function

<dl><dt>x</dt>
<dd>Fitted object of class <code>SDForest</code>.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Author

Plot performance of SDForest against number of trees — plot.SDForest

<dl>

<dt>x</dt>
<dd>Fitted object of class <code>SDForest</code>.</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd>

</dl>

plot.SDForest: Plot performance of SDForest against number of trees

Description

Usage

Value

Arguments

Author

See Also