compare_evaluate

Evaluate estimators by training nuisance functions on training set and evaluating them on test set.

Doubly robust average partial effect estimation. This implementation contains methods for adding additional smoothness to plug-in regression procedures and for estimating score functions using smoothing splines. Details of the method can be found in Harvey Klyne and Rajen D. Shah (2023) <doi:10.48550/arXiv.2308.09207>.

Harvey Klyne

drape

Doubly Robust Average Partial Effects

compare_evaluate function

<dl><dt>train</dt>
<dd>list containing vector of responses y and matrix of predictors X = (x,z).</dd>
<dt>test</dt>
<dd>list containing vector of responses y and matrix of predictors X = (x,z).</dd>
<dt>ex_setting</dt>
<dd>string "normal", "mixture2", "mixture3",
"logistic", "t4", "401k".</dd>
<dt>f_setting</dt>
<dd>string "plm", "additive", "interaction".</dd>
<dt>regression</dt>
<dd>Optional fitted regression.</dd>
<dt>sm_bw_out</dt>
<dd>Output of cv_resmooth.</dd></dl>

Arguments

Evaluate estimators by training nuisance functions on training set and evaluating them on test set. — compare_evaluate

<dl>

<dt>train</dt>
<dd>list containing vector of responses y and matrix of predictors X = (x,z).</dd>


<dt>test</dt>
<dd>list containing vector of responses y and matrix of predictors X = (x,z).</dd>


<dt>ex_setting</dt>
<dd>string "normal", "mixture2", "mixture3",
"logistic", "t4", "401k".</dd>


<dt>f_setting</dt>
<dd>string "plm", "additive", "interaction".</dd>


<dt>regression</dt>
<dd>Optional fitted regression.</dd>


<dt>sm_bw_out</dt>
<dd>Output of cv_resmooth.</dd>

</dl>

compare_evaluate: Evaluate estimators by training nuisance functions on training set and evaluating them on test set.

Description

Usage

Value

Arguments