rothenhausler_yu

Estimate the average partial effect of using the debiased lasso method of Rothenhausler and Yu,
using pre-tuned lasso penalties, for use in simulations.

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

rothenhausler_yu function

<dl><dt>X</dt>
<dd>matrix of covariates.</dd>
<dt>y</dt>
<dd>vector of responses.</dd>
<dt>f_lambda</dt>
<dd>lasso penalty for regression of y on X.</dd>
<dt>m_lambda</dt>
<dd>lasso penalty for predictor regression of the first column of X on the others.</dd></dl>

Arguments

Estimate the average partial effect of using the debiased lasso method of Rothenhausler and Yu,
using pre-tuned lasso penalties, for use in simulations. — rothenhausler_yu

<dl>

<dt>X</dt>
<dd>matrix of covariates.</dd>


<dt>y</dt>
<dd>vector of responses.</dd>


<dt>f_lambda</dt>
<dd>lasso penalty for regression of y on X.</dd>


<dt>m_lambda</dt>
<dd>lasso penalty for predictor regression of the first column of X on the others.</dd>

</dl>

Estimate the average partial effect of using the debiased lasso method of Rothenhausler and Yu,
using pre-tuned lasso penalties, for use in simulations.

rothenhausler_yu: Estimate the average partial effect of using the debiased lasso method of Rothenhausler and Yu, using pre-tuned lasso penalties, for use in simulations.

Description

Usage

Value

Arguments