mlsanityData to 14 Oct 2026

Distributed Double Machine Learning with a Serverless Architecture

arXiv 2101.04025 ↗First posted 11 Jan 2021cs.DC cs.LG stat.MLcited by 5

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Abstract (arXiv)

This paper explores serverless cloud computing for double machine learning. Being based on repeated cross-fitting, double machine learning is particularly well suited to exploit the high level of parallelism achievable with serverless computing. It allows to get fast on-demand estimations without additional cloud maintenance effort. We provide a prototype Python implementation textttDoubleML-Serverless for the estimation of double machine learning models with the serverless computing platform AWS Lambda and demonstrate its utility with a case study analyzing estimation times and costs.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 12 of its 42 references that are in the map. First posted on arXiv 11 Jan 2021.

Builds on

12 of 42 references.

References · 12

  1. 2019Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processescited by 119
  2. 2020Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimationcited by 27
  3. 2017Occupy the Cloud: Distributed Computing for the 99%cited by 17
  4. 2017Serverless Computing: Current Trends and Open Problemscited by 17
  5. 2015Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Frameworkcited by 12
  6. 2018Serverless Computing: One Step Forward, Two Steps Backcited by 12
  7. 2013Uniform Post Selection Inference for LAD Regression and Other Z-estimation problemscited by 11
  8. 2017Serving deep learning models in a serverless platformcited by 9
  9. 2019BARISTA: Efficient and Scalable Serverless Serving System for Deep Learning Prediction Servicescited by 6
  10. 2017Transformation Models in High-Dimensionscited by 2
  11. 2018Uniform Inference in High-Dimensional Gaussian Graphical Modelscited by 2
  12. 2020Estimation and Uniform Inference in Sparse High-Dimensional Additive Modelscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.

5 papers.

  1. 2021DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Pythoncited by 23
  2. 2021DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Rcited by 13
  3. 2022Coordinated Double Machine Learningcited by 4
  4. 2025Latent Variable Modeling for Robust Causal Effect Estimationcited by 0
  5. 2026Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Sparkcited by 0