dmlalg: dmlalg: double machine learning algorithms
In dmlalg: Double Machine Learning Algorithms
Description
Partially linear models with confounding variables
Partially linear mixed-effects models with repeated measurements
References
Description
The dmlalg package contains implementations of
double machine learning (DML) algorithms in R.
Partially linear models with confounding variables
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.
regsdmlEstimates the
linear parameter in a partially linear model with
confounding variables with regularized and standard
DML methods.
summary.regsdmlA summary method for objects
fitted with regsdml.
confint.regsdmlA confint method for objects
fitted with regsdml.
coef.regsdmlA coef method for objects
fitted with regsdml.
vcov.regsdmlA vcov method for objects
fitted with regsdml.
print.regsdmlA print method for objects
fitted with regsdml.
Partially linear mixed-effects models with repeated measurements
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.
mmdmlEstimates the
linear parameter in a PLMM with repeated
measurements using
double machine learning.
confint.mmdmlA confint method for objects
fitted with mmdml.
fixef.mmdmlA fixef method for objects
fitted with mmdml.
print.mmdmlA print method for objects
fitted with mmdml.
ranef.mmdmlA ranef method for objects
fitted with mmdml.
residuals.mmdmlA residuals method for objects
fitted with mmdml.
sigma.mmdmlA sigma method for objects
fitted with mmdml.
summary.mmdmlA summary method for objects
fitted with mmdml.
vcov.mmdmlA vcov method for objects
fitted with mmdml.
VarCorr.mmdmlA VarCorr method for objects
fitted with mmdml.
References
C. Emmenegger and P. Bühlmann.
Regularizing Double Machine Learning in Partially Linear Endogenous
Models, 2021. Preprint arXiv:2101.12525.
C. Emmenegger and P. Bühlmann.
Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements. Preprint arXiv:2108.13657.
dmlalg documentation built on Feb. 3, 2022, 5:08 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Partially linear models with confounding variables Partially linear mixed-effects models with repeated measurements References
Description
The dmlalg package contains implementations of
double machine learning (DML) algorithms in R.
Partially linear models with confounding variables
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.
regsdmlEstimates the linear parameter in a partially linear model with confounding variables with regularized and standard DML methods.
summary.regsdmlA
summarymethod for objects fitted withregsdml.confint.regsdmlA
confintmethod for objects fitted withregsdml.coef.regsdmlA
coefmethod for objects fitted withregsdml.vcov.regsdmlA
vcovmethod for objects fitted withregsdml.print.regsdmlA
printmethod for objects fitted withregsdml.
Partially linear mixed-effects models with repeated measurements
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.
mmdmlEstimates the linear parameter in a PLMM with repeated measurements using double machine learning.
confint.mmdmlA
confintmethod for objects fitted withmmdml.fixef.mmdmlA
fixefmethod for objects fitted withmmdml.print.mmdmlA
printmethod for objects fitted withmmdml.ranef.mmdmlA
ranefmethod for objects fitted withmmdml.residuals.mmdmlA
residualsmethod for objects fitted withmmdml.sigma.mmdmlA
sigmamethod for objects fitted withmmdml.summary.mmdmlA
summarymethod for objects fitted withmmdml.vcov.mmdmlA
vcovmethod for objects fitted withmmdml.VarCorr.mmdmlA
VarCorrmethod for objects fitted withmmdml.
References
C. Emmenegger and P. Bühlmann. Regularizing Double Machine Learning in Partially Linear Endogenous Models, 2021. Preprint arXiv:2101.12525.
C. Emmenegger and P. Bühlmann. Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements. Preprint arXiv:2108.13657.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.