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Found 20 papers, 6 papers with code
Applied Causal Inference Powered by ML and AI
An introduction to the emerging fusion of machine learning and causal inference.
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.
Label Attention Network for sequential multi-label classification: you were looking at a wrong self-attention
For example, the micro-AUC of our approach is $0. 9536$ compared to $0. 7501$ for a vanilla transformer.
Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls
This article is an introduction to machine learning for financial forecasting, planning and analysis (FP\&A).
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.
Uniform Inference in High-Dimensional Generalized Additive Models
We develop a method for uniform valid confidence bands of a nonparametric component $f_1$ in the general additive model $Y=f_1(X_1)+\ldots + f_p(X_p) + \varepsilon$ in a high-dimensional setting.
Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data
In this paper we develop a data-driven smoothing technique for high-dimensional and non-linear panel data models.
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.
A Self-Attention Network for Hierarchical Data Structures with an Application to Claims Management
We show that the proposed methods outperform bag-of-words based models, hand designed features, and models based on convolutional neural networks, on a data set of two million health care claims.
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.
Estimation and Inference of Treatment Effects with $L_2$-Boosting in High-Dimensional Settings
In this paper, we provide results for valid inference after post- or orthogonal $L_2$-Boosting is used for variable selection.
Transformation Models in High-Dimensions
Transformation models are a very important tool for applied statisticians and econometricians.
$L_2$Boosting for Economic Applications
In the recent years more and more high-dimensional data sets, where the number of parameters $p$ is high compared to the number of observations $n$ or even larger, are available for applied researchers.
hdm: High-Dimensional Metrics
In this article the package High-dimensional Metrics (\texttt{hdm}) is introduced.
High-Dimensional Metrics in R
The package High-dimensional Metrics (\Rpackage{hdm}) is an evolving collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models.
High-Dimensional $L_2$Boosting: Rate of Convergence
Finally, we present simulation studies and applications to illustrate the relevance of our theoretical results and to provide insights into the practical aspects of boosting.
