45 citations · 132 across the 7 of their papers we have counts for
7 papers
Vine copula based knockoff generation for high-dimensional controlled variable selection
Malte S. Kurz
Vine copulas are a flexible tool for high-dimensional dependence modeling. In this article, we discuss the generation of approximate model-X knockoffs with vine copulas. It is show…
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python
Philipp Bach, Victor Chernozhukov, Malte S. Kurz +1
DoubleML is an open-source Python library implementing the double machine learning framework of Chernozhukov et al. (2018) for a variety of causal models. It contains functionaliti…
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R
Philipp Bach, Victor Chernozhukov, Malte S. Kurz +2
The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov et al. (2018). It provides functionalities to estimate parameters in causal models…
Distributed Double Machine Learning with a Serverless Architecture
Malte S. Kurz
This paper explores serverless cloud computing for double machine learning. Being based on repeated cross-fitting, double machine learning is particularly well suited to exploit th…
Testing the simplifying assumption in high-dimensional vine copulas
Malte S. Kurz, Fabian Spanhel
Testing the simplifying assumption in high-dimensional vine copulas is a difficult task. Tests must be based on estimated observations and check constraints on high-dimensional dis…
The partial copula: Properties and associated dependence measures
Fabian Spanhel, Malte S. Kurz
The partial correlation coefficient is a commonly used measure to assess the conditional dependence between two random variables. We provide a thorough explanation of the partial c…
The partial vine copula: A dependence measure and approximation based on the simplifying assumption
Fabian Spanhel, Malte S. Kurz
Simplified vine copulas (SVCs), or pair-copula constructions, have become an important tool in high-dimensional dependence modeling. So far, specification and estimation of SVCs ha…