prune.SDTree

Removes all nodes that did not improve the loss by more than cp times the initial loss. 
Either by themselves or by one of their successors. Note that the tree is pruned in place.
If you intend to keep the original tree, make a copy of it before pruning.

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

prune.SDTree function

<dl><dt>object</dt>
<dd>an SDTree object</dd>
<dt>cp</dt>
<dd>Complexity parameter, the higher the value the more nodes are pruned.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Author

Prune an SDTree — prune.SDTree

<dl>

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


<dt>cp</dt>
<dd>Complexity parameter, the higher the value the more nodes are pruned.</dd>


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

</dl>

prune.SDTree: Prune an SDTree

Description

Usage

Value

Arguments

Author

See Also

Examples