predict.SDAM

Predicts the response for new data 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.SDAM function

<dl><dt>object</dt>
<dd>Fitted object of class <code>SDAM</code>.</dd>
<dt>newdata</dt>
<dd>New test data of class <code>data.frame</code> containing
the covariates for which to predict the response.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Author

Predictions for SDAM — predict.SDAM

<dl>

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


<dt>newdata</dt>
<dd>New test data of class <code>data.frame</code> containing
the covariates for which to predict the response.</dd>


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

</dl>

predict.SDAM: Predictions for SDAM

Description

Usage

Value

Arguments

Author

See Also

Examples