mlsanityData to 7 Oct 2026

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

arXiv 2104.03220 ↗First posted 7 Apr 2021stat.ML cs.LG econ.EMcited by 23

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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.

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

Builds on

6 of 19 references.

References · 6

  1. 2019Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processescited by 118
  2. 2020CausalML: Python Package for Causal Machine Learningcited by 74
  3. 2017Orthogonal Machine Learning: Power and Limitationscited by 28
  4. 2021DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Rcited by 13
  5. 2020Debiased Off-Policy Evaluation for Recommendation Systemscited by 9
  6. 2021Distributed Double Machine Learning with a Serverless Architecturecited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 7; 2025: 6; 2026: 6.

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  1. 2024Causal machine learning for predicting treatment outcomescited by 73
  2. 2024DoubleMLDeep: Estimation of Causal Effects with Multimodal Datacited by 8
  3. 2022Coordinated Double Machine Learningcited by 4
  4. 2025Adjustment for Confounding using Pre-Trained Representationscited by 3
  5. 2023Causally Learning an Optimal Rework Policycited by 2
  6. 2023OpportunityFinder: A Framework for Automated Causal Inferencecited by 2
  7. 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Datacited by 2
  8. 2024Interventional Processes for Causal Uncertainty Quantificationcited by 2
  9. 2022HiPart: Hierarchical Divisive Clustering Toolboxcited by 1
  10. 2024Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?cited by 1
  11. 2024What if? Causal Machine Learning in Supply Chain Risk Managementcited by 1
  12. 2025ORCA: ORchestrating Causal Agentcited by 1
11 more
  1. 2024Double Machine Learning for Adaptive Causal Representation in High-Dimensional Datacited by 0
  2. 2025Practical programming research of Linear DML model based on the simplest Python code: From the standpoint of novice researcherscited by 0
  3. 2025Forests for Differences: Robust Causal Inference Beyond Parametric DiDcited by 0
  4. 2025LILI clustering algorithm: Limit Inferior Leaf Interval Integrated into Causal Forest for Causal Interferencecited by 0
  5. 2025When Algorithms Manage Humans: A Double Machine Learning Approach to Estimating Nonlinear Effects of Algorithmic Control on Gig Worker Performance and Wellbeingcited by 0
  6. 2026genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regressioncited by 0
  7. 2026Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inferencecited by 0
  8. 2026Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonalitycited by 0
  9. 2026From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Researchcited by 0
  10. 2026CAS: A Causal Attribution Score for Local and Global Explainable Artificial Intelligencecited by 0
  11. 2026Actionable Insights from Observational Data: The Case of Advanced Classes in K-12 Educationcited by 0