plot.dml_with_smoother

<code>plot</code> method for class <code>dml_with_smoother</code>

Many treatment effect estimators can be written as weighted outcomes.
These weights have established use cases like checking covariate balancing via packages like 'cobalt'.
This package takes the original estimator objects and outputs these outcome weights.
It builds on the general framework of Knaus (2024) <doi:10.48550/arXiv.2411.11559>.
This version is compatible with the 'grf' package and provides an internal implementation of Double Machine Learning.

Knaus Michael C.

OutcomeWeights

Outcome Weights of Treatment Effect Estimators

Michael C. Knaus

Henri Pfleiderer

plot.dml_with_smoother function

<dl><dt>x</dt>
<dd>Object of class <code>dml_with_smoother</code>.</dd>
<dt>...</dt>
<dd>Pass generic <code><a href="/link/plot?package=OutcomeWeights&version=0.1.1" data-mini-rdoc="OutcomeWeights::plot">plot</a></code> options.</dd>
<dt>alpha</dt>
<dd>Significance level for confidence intervals (default 0.05).</dd>
<dt>contrast</dt>
<dd>Shows the differences between the coefficients.</dd></dl>

Arguments

plot method for class dml_with_smoother — plot.dml_with_smoother

<dl>

<dt>x</dt>
<dd>Object of class <code>dml_with_smoother</code>.</dd>


<dt>...</dt>
<dd>Pass generic <code><a href='https://rdrr.io/r/base/plot.html'>plot</a></code> options.</dd>


<dt>alpha</dt>
<dd>Significance level for confidence intervals (default 0.05).</dd>


<dt>contrast</dt>
<dd>Shows the differences between the coefficients.</dd>

</dl>

plot.dml_with_smoother: `plot` method for class `dml_with_smoother`

Description

Usage

Value

Arguments