# A Tutorial on Doubly Robust Learning for Causal Inference

arXiv: https://arxiv.org/abs/2406.00853 · HTML: https://mlsanity.com/paper/2406.00853/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 2 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population](https://mlsanity.com/paper/2201.06595/) (2022)
- [Double Machine Learning Methods for Estimating Average Treatment Effects: A Comparative Study](https://mlsanity.com/paper/2204.10969/) (2022)
- [Efficiency-improved doubly robust estimation with non-confounding predictive covariates](https://mlsanity.com/paper/2402.14438/) (2024)

## Built on by (most cited first, 2 of 2)

- [Causal-Copilot: An Autonomous Causal Analysis Agent](https://mlsanity.com/paper/2504.13263/) (2025)
- [Correcting test set contamination by spiking the training data](https://mlsanity.com/paper/2605.24818/) (2026)

## Abstract (arXiv)

Doubly robust learning offers a robust framework for causal inference from observational data by integrating propensity score and outcome modeling. Despite its theoretical appeal, practical adoption remains limited due to perceived complexity and inaccessible software. This tutorial aims to demystify doubly robust methods and demonstrate their application using the EconML package. We provide an introduction to causal inference, discuss the principles of outcome modeling and propensity scores, and illustrate the doubly robust approach through simulated case studies. By simplifying the methodology and offering practical coding examples, we intend to make doubly robust learning accessible to researchers and practitioners in data science and statistics.