repfit

repfit.Repeated

Fitting the data into the AIPW object with cross-fitting repeatedly to obtain multiple estimates from repetitions to avoid randomness due to splits in cross-fitting

The 'AIPW' package implements the augmented inverse probability weighting, a doubly robust estimator, for average causal effect estimation with user-defined stacked machine learning algorithms. To cite the 'AIPW' package, please use: "Yongqi Zhong, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi (2021). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology. <doi:10.1093/aje/kwab207>". Visit: <https://yqzhong7.github.io/AIPW/> for more information.

Yongqi Zhong

AIPW

Augmented Inverse Probability Weighting

Ashley Naimi

Gabriel Conzuelo

Edward Kennedy

repfit function

<dl><dt>num_reps</dt>
<dd>Integer. Number of repetition of cross-fitting procedures (<code>fit()</code> or <code>stratified_fit()</code> see blow).</dd>
<dt>stratified</dt>
<dd>Boolean. <code>stratified = TRUE</code> will use <code>stratified_fit()</code> in the AIPW object to cross-fitting.</dd></dl>

Arguments

<code>$repfit(num_reps = 20, stratified = FALSE)</code>

R6 Usage

Fit the data to the AIPW object repeatedly — repfit

<dl>

<dt>num_reps</dt>
<dd>Integer. Number of repetition of cross-fitting procedures (<code>fit()</code> or <code>stratified_fit()</code> see blow).</dd>


<dt>stratified</dt>
<dd>Boolean. <code>stratified = TRUE</code> will use <code>stratified_fit()</code> in the AIPW object to cross-fitting.</dd>

</dl>

<code>$repfit(num_reps = 20, stratified = FALSE)</code>

repfit: Fit the data to the AIPW object repeatedly

Description

Value

Arguments

R6 Usage

References

See Also