dataHolder-class

internal

Uses optimal transport distances to find probabilistic
matching estimators for causal inference.
These methods are described in Dunipace, Eric (2021) <arXiv:2109.01991>.
The package will build the weights, estimate treatment effects, and
calculate confidence intervals via the methods described in the paper.
The package also supports several other methods as described in the help
files.

Eric Dunipace

causalOT

Optimal Transport Weights for Causal Inference

dataHolder-class function

<dl>
<dt><code>x</code></dt>
<dd>matrix. A matrix of confounders.</dd></dl>
<dt><code>z</code></dt>
<dd>integer. The treatment indicator, \(z_i \in \{0,1\}\).</dd>
<dt><code>y</code></dt>
<dd>numeric. The outcome data.</dd>
<dt><code>n0</code></dt>
<dd>integer. The number of observations where z==0</dd>
<dt><code>n1</code></dt>
<dd>integer. The number of observations where z==1</dd>
<dt><code>weights</code></dt>
<dd>numeric. The empirical distribution of the full sample.</dd>

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dataHolder-class — dataHolder-class

<dl>
<dt><code>x</code></dt>
<dd>matrix. A matrix of confounders.</dd>


<dt><code>z</code></dt>
<dd>integer. The treatment indicator, \(z_i \in \{0,1\}\).</dd>


<dt><code>y</code></dt>
<dd>numeric. The outcome data.</dd>


<dt><code>n0</code></dt>
<dd>integer. The number of observations where z==0</dd>


<dt><code>n1</code></dt>
<dd>integer. The number of observations where z==1</dd>


<dt><code>weights</code></dt>
<dd>numeric. The empirical distribution of the full sample.</dd>


</dl>

dataHolder-class: dataHolder-class

Description

Arguments

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