calculate_ps_weights

Calculates propensity score weights.

It is often advantageous to test a hypothesis more than once
in the context of propensity score analysis (Rosenbaum, 2012)
<doi:10.1093/biomet/ass032>. The functions in this package facilitate
bootstrapping for propensity score analysis (PSA). By default,
bootstrapping using two classification tree methods (using 'rpart' and
'ctree' functions), two matching methods (using 'Matching' and
'MatchIt' packages), and stratification with logistic regression. A
framework is described for users to implement additional propensity
score methods. Visualizations are emphasized for diagnosing balance;
exploring the correlation relationships between bootstrap samples and
methods; and to summarize results.

Jason Bryer

PSAboot

Bootstrapping for Propensity Score Analysis

calculate_ps_weights function

<dl><dt>treatment</dt>
<dd>a logical vector for treatment status.</dd>
<dt>ps</dt>
<dd>numeric vector of propensity scores</dd>
<dt>estimand</dt>
<dd>character string indicating which estimand to be used. Possible
values are 
ATE (average treatment effect), 
ATT (average treatment effect for the treated), 
ATC (average treatment effect for the controls), 
ATM (Average Treatment Effect Among the Evenly Matched), 
ATO (Average Treatment Effect Among the Overlap Population)</dd></dl>

Arguments

Calculates propensity score weights. — calculate_ps_weights

<dl>

<dt>treatment</dt>
<dd>a logical vector for treatment status.</dd>


<dt>ps</dt>
<dd>numeric vector of propensity scores</dd>


<dt>estimand</dt>
<dd>character string indicating which estimand to be used. Possible
values are 
ATE (average treatment effect), 
ATT (average treatment effect for the treated), 
ATC (average treatment effect for the controls), 
ATM (Average Treatment Effect Among the Evenly Matched), 
ATO (Average Treatment Effect Among the Overlap Population)</dd>

</dl>

calculate_ps_weights: Calculates propensity score weights.

Description

Usage

Arguments