example_data_mmdml

Numeric vector of length 1 or 2 representing the linear
 coefficient/fixed effects of the model.

beta0

Number of experimental units. Equals 10 per default.

Expected number of observations per experimental unit,
 needs to be at least 5. Equals 5 per default.

Generate data from a partially linear mixed-effects model with one or two
 fixed effects, 2 random effects, and 3 nonparametric variables.
 The true underlying function of the nonparametric variables are step
 functions.
 The random effects and error terms are from a Gaussian distribution.

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.

example_data_mmdml: Generate data from partially linear mixed-effects model

Description

Usage

Arguments

Value

See Also

Examples