predict_individual_fj

Predicts the contribution of an individual component j using a fitted SDAM.

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

predict_individual_fj function

<dl><dt>object</dt>
<dd>Fitted object of class <code>SDAM</code>.</dd>
<dt>j</dt>
<dd>Which component to evaluate.</dd>
<dt>x</dt>
<dd>New numeric data to predict for.</dd></dl>

Arguments

Author

Predictions of individual component functions for SDAM — predict_individual_fj

<dl>

<dt>object</dt>
<dd>Fitted object of class <code>SDAM</code>.</dd>


<dt>j</dt>
<dd>Which component to evaluate.</dd>


<dt>x</dt>
<dd>New numeric data to predict for.</dd>

</dl>

predict_individual_fj: Predictions of individual component functions for SDAM

Description

Usage

Value

Arguments

Author

See Also

Examples