predict.cw_superlearner

Predictions from the internal nuisance learner

internal

Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects based on different identification strategies (unconfoundedness, instruments, difference-in-differences, regression discontinuity designs).

Hugo Bodory

causalweight

Estimation Methods for Causal Inference Based on Inverse
Probability Weighting and Doubly Robust Estimation

Martin Huber

Jannis Kueck

predict.cw_superlearner function

<dl><dt>object</dt>
<dd>Fitted object of class <code>cw_superlearner</code>.</dd>
<dt>newdata</dt>
<dd>Covariates for prediction.</dd>
<dt>onlySL</dt>
<dd>Ignored; kept for compatibility with existing call sites.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Predictions from the internal nuisance learner — predict.cw_superlearner

<dl>

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


<dt>newdata</dt>
<dd>Covariates for prediction.</dd>


<dt>onlySL</dt>
<dd>Ignored; kept for compatibility with existing call sites.</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

predict.cw_superlearner: Predictions from the internal nuisance learner

Description

Usage

Value

Arguments