# DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R

arXiv: https://arxiv.org/abs/2103.09603 · HTML: https://mlsanity.com/paper/2103.09603/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 79 references that are in the map.
- First posted on arXiv 17 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [De-biasing the Lasso: Optimal Sample Size for Gaussian Designs](https://mlsanity.com/paper/1508.02757/) (2015)
- [Program Evaluation and Causal Inference with High-Dimensional Data](https://mlsanity.com/paper/1311.2645/) (2013)
- [Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory](https://mlsanity.com/paper/1301.4240/) (2013)
- [High-Dimensional Inference: Confidence Intervals, p-Values and R-Software hdi](https://mlsanity.com/paper/1408.4026/) (2014)
- [Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Framework](https://mlsanity.com/paper/1512.07619/) (2015)
- [Uniform Post Selection Inference for LAD Regression and Other Z-estimation problems](https://mlsanity.com/paper/1304.0282/) (2013)
- [Double machine learning for sample selection models](https://mlsanity.com/paper/2012.00745/) (2020)
- [hdm: High-Dimensional Metrics](https://mlsanity.com/paper/1608.00354/) (2016)
- [Distributed Double Machine Learning with a Serverless Architecture](https://mlsanity.com/paper/2101.04025/) (2021)
- [High-dimensional inference in misspecified linear models](https://mlsanity.com/paper/1503.06426/) (2015)
- [Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R)](https://mlsanity.com/paper/1809.04951/) (2018)

## Built on by (most cited first, 13 of 13)

- [DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python](https://mlsanity.com/paper/2104.03220/) (2021)
- [DoubleMLDeep: Estimation of Causal Effects with Multimodal Data](https://mlsanity.com/paper/2402.01785/) (2024)
- [Double Machine Learning for Static Panel Models with Fixed Effects](https://mlsanity.com/paper/2312.08174/) (2023)
- [Causally Learning an Optimal Rework Policy](https://mlsanity.com/paper/2306.04223/) (2023)
- [Generalized Kernel Regularized Least Squares](https://mlsanity.com/paper/2209.14355/) (2022)
- [Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?](https://mlsanity.com/paper/2406.11308/) (2024)
- [What if? Causal Machine Learning in Supply Chain Risk Management](https://mlsanity.com/paper/2408.13556/) (2024)
- [Three Routes to One Answer: Reconciling AIPW, TMLE, and Double Machine Learning for Applied Researchers](https://mlsanity.com/paper/2609.26142/) (2026)
- [TSCI: two stage curvature identification for causal inference with invalid instruments](https://mlsanity.com/paper/2304.00513/) (2023)
- [Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data](https://mlsanity.com/paper/2411.14665/) (2024)
- [xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R](https://mlsanity.com/paper/2512.15965/) (2025)
- [An Integrated Causal Inference Framework for Traffic Safety Modeling with Semantic Street-View Visual Features](https://mlsanity.com/paper/2602.13339/) (2026)
- [genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression](https://mlsanity.com/paper/2602.17543/) (2026)

## Abstract (arXiv)

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 based on machine learning methods. The double machine learning framework consist of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be performed by various state-of-the-art machine learning methods that are available in the mlr3 ecosystem. DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models and their extensions to instrumental variable estimation. The object-oriented implementation of DoubleML enables a high flexibility for the model specification and makes it easily extendable. This paper serves as an introduction to the double machine learning framework and the R package DoubleML. In reproducible code examples with simulated and real data sets, we demonstrate how DoubleML users can perform valid inference based on machine learning methods.