sbwOptions

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

sbwOptions function

<dl><dt>delta</dt>
<dd>A number or vector of tolerances for the balancing functions. Default is NULL which will use a grid search</dd>
<dt>grid.length</dt>
<dd>The number of values to try in the grid search</dd>
<dt>nboot</dt>
<dd>The number of bootstrap samples to run during the grid search.</dd>
<dt>...</dt>
<dd>Arguments passed on to osqpSettings()</dd></dl>

Arguments

This method will balance functions of the covariates within some tolerance, $\delta$. For these functions $B$, we will desire
$$\frac{\sum_{i: Z_i = 0} w_i B(x_i) - \sum_{j: Z_j = 1} B(x_j)/n_1}{\sigma} \leq \delta$$, where in this case we are targeting balance with the treatment group for the ATT. $\sigma$ is the pooled standard deviation prior to balancing.

Function balancing

Options for the SBW method — sbwOptions

<dl>

<dt>delta</dt>
<dd>A number or vector of tolerances for the balancing functions. Default is NULL which will use a grid search</dd>


<dt>grid.length</dt>
<dd>The number of values to try in the grid search</dd>


<dt>nboot</dt>
<dd>The number of bootstrap samples to run during the grid search.</dd>


<dt>...</dt>
<dd>Arguments passed on to osqpSettings()</dd>

</dl>

sbwOptions: Options for the SBW method

Description

Usage

Value

Arguments

Function balancing

Examples