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

arXiv: https://arxiv.org/abs/2104.03220 · HTML: https://mlsanity.com/paper/2104.03220/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes](https://mlsanity.com/paper/1908.08526/) (2019)
- [CausalML: Python Package for Causal Machine Learning](https://mlsanity.com/paper/2002.11631/) (2020)
- [Orthogonal Machine Learning: Power and Limitations](https://mlsanity.com/paper/1711.00342/) (2017)
- [DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R](https://mlsanity.com/paper/2103.09603/) (2021)
- [Debiased Off-Policy Evaluation for Recommendation Systems](https://mlsanity.com/paper/2002.08536/) (2020)
- [Distributed Double Machine Learning with a Serverless Architecture](https://mlsanity.com/paper/2101.04025/) (2021)

## Built on by (most cited first, 23 of 23)

- [Causal machine learning for predicting treatment outcomes](https://mlsanity.com/paper/2410.08770/) (2024)
- [DoubleMLDeep: Estimation of Causal Effects with Multimodal Data](https://mlsanity.com/paper/2402.01785/) (2024)
- [Coordinated Double Machine Learning](https://mlsanity.com/paper/2206.00885/) (2022)
- [Adjustment for Confounding using Pre-Trained Representations](https://mlsanity.com/paper/2506.14329/) (2025)
- [Causally Learning an Optimal Rework Policy](https://mlsanity.com/paper/2306.04223/) (2023)
- [OpportunityFinder: A Framework for Automated Causal Inference](https://mlsanity.com/paper/2309.13103/) (2023)
- [Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data](https://mlsanity.com/paper/2406.06452/) (2024)
- [Interventional Processes for Causal Uncertainty Quantification](https://mlsanity.com/paper/2410.14483/) (2024)
- [HiPart: Hierarchical Divisive Clustering Toolbox](https://mlsanity.com/paper/2209.08680/) (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)
- [ORCA: ORchestrating Causal Agent](https://mlsanity.com/paper/2508.21304/) (2025)
- [Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data](https://mlsanity.com/paper/2411.14665/) (2024)
- [Practical programming research of Linear DML model based on the simplest Python code: From the standpoint of novice researchers](https://mlsanity.com/paper/2502.16172/) (2025)
- [Forests for Differences: Robust Causal Inference Beyond Parametric DiD](https://mlsanity.com/paper/2505.09706/) (2025)
- [LILI clustering algorithm: Limit Inferior Leaf Interval Integrated into Causal Forest for Causal Interference](https://mlsanity.com/paper/2507.03271/) (2025)
- [When Algorithms Manage Humans: A Double Machine Learning Approach to Estimating Nonlinear Effects of Algorithmic Control on Gig Worker Performance and Wellbeing](https://mlsanity.com/paper/2512.22290/) (2025)
- [genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression](https://mlsanity.com/paper/2602.17543/) (2026)
- [Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference](https://mlsanity.com/paper/2603.13662/) (2026)
- [Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality](https://mlsanity.com/paper/2605.01907/) (2026)
- [From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Research](https://mlsanity.com/paper/2607.00512/) (2026)
- [CAS: A Causal Attribution Score for Local and Global Explainable Artificial Intelligence](https://mlsanity.com/paper/2608.12555/) (2026)
- [Actionable Insights from Observational Data: The Case of Advanced Classes in K-12 Education](https://mlsanity.com/paper/2609.23836/) (2026)

## Abstract (arXiv)

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 functionalities for valid statistical inference on causal parameters when the estimation of nuisance parameters is based on machine learning methods. The object-oriented implementation of DoubleML provides a high flexibility in terms of model specifications and makes it easily extendable. The package is distributed under the MIT license and relies on core libraries from the scientific Python ecosystem: scikit-learn, numpy, pandas, scipy, statsmodels and joblib. Source code, documentation and an extensive user guide can be found at https://github.com/DoubleML/doubleml-for-py and https://docs.doubleml.org.