Search Results for author:
Found 7 papers, 2 papers with code
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
First, we assess the importance of data splitting schemes for tuning ML learners within Double Machine Learning.
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
This paper explores the use of unstructured, multimodal data, namely text and images, in causal inference and treatment effect estimation.
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python
DoubleML is an open-source Python library implementing the double machine learning framework of Chernozhukov et al. (2018) for a variety of causal models.
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R
This paper serves as an introduction to the double machine learning framework and the R package DoubleML.
Estimation and Uniform Inference in Sparse High-Dimensional Additive Models
We develop a novel method to construct uniformly valid confidence bands for a nonparametric component $f_1$ in the sparse additive model $Y=f_1(X_1)+\ldots + f_p(X_p) + \varepsilon$ in a high-dimensional setting.
Closing the U.S. gender wage gap requires understanding its heterogeneity
In 2016, the majority of full-time employed women in the U. S. earned significantly less than comparable men.
Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R)
Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important.
