# Distributed Double Machine Learning with a Serverless Architecture

arXiv: https://arxiv.org/abs/2101.04025 · HTML: https://mlsanity.com/paper/2101.04025/

## 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 (most cited first, 12 of 12)

- [Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes](https://mlsanity.com/paper/1908.08526/) (2019)
- [Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation](https://mlsanity.com/paper/2002.07285/) (2020)
- [Occupy the Cloud: Distributed Computing for the 99%](https://mlsanity.com/paper/1702.04024/) (2017)
- [Serverless Computing: Current Trends and Open Problems](https://mlsanity.com/paper/1706.03178/) (2017)
- [Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Framework](https://mlsanity.com/paper/1512.07619/) (2015)
- [Serverless Computing: One Step Forward, Two Steps Back](https://mlsanity.com/paper/1812.03651/) (2018)
- [Uniform Post Selection Inference for LAD Regression and Other Z-estimation problems](https://mlsanity.com/paper/1304.0282/) (2013)
- [Serving deep learning models in a serverless platform](https://mlsanity.com/paper/1710.08460/) (2017)
- [BARISTA: Efficient and Scalable Serverless Serving System for Deep Learning Prediction Services](https://mlsanity.com/paper/1904.01576/) (2019)
- [Transformation Models in High-Dimensions](https://mlsanity.com/paper/1712.07364/) (2017)
- [Uniform Inference in High-Dimensional Gaussian Graphical Models](https://mlsanity.com/paper/1808.10532/) (2018)
- [Estimation and Uniform Inference in Sparse High-Dimensional Additive Models](https://mlsanity.com/paper/2004.01623/) (2020)

## Built on by (most cited first, 5 of 5)

- [DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python](https://mlsanity.com/paper/2104.03220/) (2021)
- [DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R](https://mlsanity.com/paper/2103.09603/) (2021)
- [Coordinated Double Machine Learning](https://mlsanity.com/paper/2206.00885/) (2022)
- [Latent Variable Modeling for Robust Causal Effect Estimation](https://mlsanity.com/paper/2508.20259/) (2025)
- [Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark](https://mlsanity.com/paper/2604.25061/) (2026)

## 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.