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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.
Causally Learning an Optimal Rework Policy
In this paper, we apply double/debiased machine learning (DML) to estimate the conditional treatment effect of a rework step during the color conversion process in opto-electronic semiconductor manufacturing on the final product yield.
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.
Uniform Inference in High-Dimensional Gaussian Graphical Models
Graphical models have become a very popular tool for representing dependencies within a large set of variables and are key for representing causal structures.
Transformation Models in High-Dimensions
Transformation models are a very important tool for applied statisticians and econometricians.
