plot.partDependence

This function plots the partial dependence of a model on a single variable.

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.partDependence function

<dl><dt>x</dt>
<dd>An object of class <code>partDependence</code> returned by <code>partDependence</code>.</dd>
<dt>n_examples</dt>
<dd>Number of examples to plot in addition to the average prediction.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Author

Plot partial dependence — plot.partDependence

<dl>

<dt>x</dt>
<dd>An object of class <code>partDependence</code> returned by <code>partDependence</code>.</dd>


<dt>n_examples</dt>
<dd>Number of examples to plot in addition to the average prediction.</dd>


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

</dl>

plot.partDependence: Plot partial dependence

Description

Usage

Value

Arguments

Author

See Also