eval_Diptw_g

Evaluate extra piece of the influence function for the IPTW

Targeted minimum loss-based estimators of counterfactual means and
causal effects that are doubly-robust with respect both to consistency and
asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ
van der Laan (2014), <doi:10.1515/ijb-2012-0038>).

David Benkeser

drtmle

Doubly-Robust Nonparametric Estimation and Inference

Nima Hejazi

eval_Diptw_g function

<dl><dt>A</dt>
<dd>A vector of binary treatment assignment (assumed to be equal to 0 or
1)</dd>
<dt>DeltaA</dt>
<dd>Indicator of missing treatment (assumed to be equal to 0 if
missing 1 if observed)</dd>
<dt>DeltaY</dt>
<dd>Indicator of missing outcome (assumed to be equal to 0 if
missing 1 if observed)</dd>
<dt>Qrn</dt>
<dd>List of estimated reduced-dimension outcome regression evaluated
at observations</dd>
<dt>gn</dt>
<dd>List of estimated propensity scores evaluated at observations</dd>
<dt>a_0</dt>
<dd>Vector of values to return marginal mean</dd></dl>

Arguments

Evaluate extra piece of the influence function for the IPTW — eval_Diptw_g

<dl>

<dt>A</dt>
<dd>A vector of binary treatment assignment (assumed to be equal to 0 or
1)</dd>


<dt>DeltaA</dt>
<dd>Indicator of missing treatment (assumed to be equal to 0 if
missing 1 if observed)</dd>


<dt>DeltaY</dt>
<dd>Indicator of missing outcome (assumed to be equal to 0 if
missing 1 if observed)</dd>


<dt>Qrn</dt>
<dd>List of estimated reduced-dimension outcome regression evaluated
at observations</dd>


<dt>gn</dt>
<dd>List of estimated propensity scores evaluated at observations</dd>


<dt>a_0</dt>
<dd>Vector of values to return marginal mean</dd>

</dl>

eval_Diptw_g: Evaluate extra piece of the influence function for the IPTW

Description

Usage

Arguments