summary.regsdml

An object of class <code>regsdml</code>. This object usually
 results from a function call to <code><a rd-options="" href="/link/regsdml?package=dmlalg&version=1.0.2" data-mini-rdoc="dmlalg::regsdml">regsdml</a></code>.

object

A boolean. If <code>TRUE</code>, the results
 of the <code>regsDML</code> method are returned.

print_regsDML

A boolean. If <code>TRUE</code>, the results
 of the safety device are returned.

print_safety

A boolean. If <code>TRUE</code>, the results
 of the <code>DML</code> method are returned.

print_DML

A boolean. If <code>TRUE</code>, the results
 of the <code>regDML</code> method with the optimal choice of <code>gamma</code>
 (including the factor <code>a_N</code>)
 are returned.

print_regDML

A boolean. If <code>TRUE</code>, the results
 specified by <code>parm</code> below are returned.

print_regDML_all_gamma

A vector containing the indices for which <code>gamma</code>-values
 the results of the regularized DML estimator, whose results
 are stored in the list <code>regDML_all_gamma_statistics</code> of <code>object</code>,
 should be included in the summary output. If <code>parm</code> is specified,
 it is not necessary to specify <code>print_regDML_all_gamma</code>.

parm

A boolean. If <code>TRUE</code>, the variance-covariance
 matrices of the coefficient estimates are displayed.

correlation

A boolean. If <code>TRUE</code>, the <code>gamma</code>-values
 are printed in brackets where the respective regularization methods
 achieved their optimum.

print_gamma

Further arguments passed to or from other methods.

This is a method for the class <code>regsdml</code>. It summarizes
 objects of class <code>regsdml</code>, which typically result from a function
 call to <code><a rd-options="" href="/link/regsdml?package=dmlalg&version=1.0.2" data-mini-rdoc="dmlalg::regsdml">regsdml</a></code>.

Implementation of double machine learning (DML) algorithms in R,
based on Emmenegger and Buehlmann (2021) "Regularizing Double Machine Learning
in Partially Linear Endogenous Models" <arXiv:2101.12525> and Emmenegger and
Buehlmann (2021) <arXiv:2108.13657> "Double Machine Learning for Partially
Linear Mixed-Effects Models with Repeated Measurements".
First part: our goal is to perform inference for the linear parameter in partially
linear models with confounding variables.
The standard DML estimator of the linear parameter has a two-stage least
squares interpretation, which can lead to a large variance and overwide
confidence intervals.
We apply regularization to reduce the variance of the estimator,
which produces narrower confidence intervals that are approximately valid.
Nuisance terms can be flexibly estimated with machine learning algorithms.
Second part: our goal is to estimate and perform inference for the linear
coefficient in a partially linear mixed-effects model
with DML. Machine learning algorithms allows us to incorporate more
complex interaction structures and high-dimensional variables.

Corinne Emmenegger

dmlalg

Double Machine Learning Algorithms

Peter Buehlmann

summary.regsdml function

An object of class <code>regsdml</code>. This object usually
 results from a function call to <code><a rd-options='' href='regsdml'>regsdml</a></code>.

This is a method for the class <code>regsdml</code>. It summarizes
 objects of class <code>regsdml</code>, which typically result from a function
 call to <code><a rd-options='' href='regsdml'>regsdml</a></code>.

summary.regsdml: Summarizing regsdml fits

Description

Usage

Arguments

Value

See Also

Examples