estimate_model

Function to estimate outcome models

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

estimate_model function

<dl><dt>data</dt>
<dd>A <code>dataHolder()</code> object</dd>
<dt>causalWeights</dt>
<dd>A causalWeights object</dd>
<dt>model.function</dt>
<dd>The model function passed by the user</dd>
<dt>separate.estimation</dt>
<dd>TRUE or FALSE, should models be estimated separately in each group?</dd>
<dt>...</dt>
<dd>Extra agruments passed to the predict functions</dd></dl>

Arguments

Function to estimate outcome models — estimate_model

<dl>

<dt>data</dt>
<dd>A <code>dataHolder()</code> object</dd>


<dt>causalWeights</dt>
<dd>A causalWeights object</dd>


<dt>model.function</dt>
<dd>The model function passed by the user</dd>


<dt>separate.estimation</dt>
<dd>TRUE or FALSE, should models be estimated separately in each group?</dd>


<dt>...</dt>
<dd>Extra agruments passed to the predict functions</dd>

</dl>

estimate_model: Function to estimate outcome models

Description

Usage

Value

Arguments