# Double Machine Learning Methods for Estimating Average Treatment Effects: A Comparative Study

arXiv: https://arxiv.org/abs/2204.10969 · HTML: https://mlsanity.com/paper/2204.10969/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 58 references that are in the map.
- First posted on arXiv 23 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms](https://mlsanity.com/paper/1711.07137/) (2017)
- [Propensity Score Matching and Subclassification in Observational Studies with Multi-level Treatments](https://mlsanity.com/paper/1508.06948/) (2015)
- [Doubly robust matching estimators for high dimensional confounding adjustment](https://mlsanity.com/paper/1612.00424/) (2016)
- [Identifying Principal Stratum Causal Effects Conditional on a Post-treatment Intermediate Response](https://mlsanity.com/paper/2103.04175/) (2021)
- [Doubly Robust Inference when Combining Probability and Non-probability Samples with High-dimensional Data](https://mlsanity.com/paper/1903.05212/) (2019)
- [Outcome-Adjusted Balance Measure for Generalized Propensity Score Model Selection](https://mlsanity.com/paper/2107.12487/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Tutorial on Doubly Robust Learning for Causal Inference](https://mlsanity.com/paper/2406.00853/) (2024)
- [Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes](https://mlsanity.com/paper/2301.06021/) (2023)
- [Preference-based Conditional Treatment Effects and Policy Learning](https://mlsanity.com/paper/2602.03823/) (2026)
- [Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects](https://mlsanity.com/paper/2403.03240/) (2024)
- [Rate doubly robust estimation for weighted average treatment effects](https://mlsanity.com/paper/2509.14502/) (2025)
- [Integrating RCTs, RWD, AI/ML and Statistics: Next-Generation Evidence Synthesis](https://mlsanity.com/paper/2511.19735/) (2025)

## Abstract (arXiv)

Observational cohort studies are increasingly being used for comparative effectiveness research to assess the safety of therapeutics. Recently, various doubly robust methods have been proposed for average treatment effect estimation by combining the treatment model and the outcome model via different vehicles, such as matching, weighting, and regression. The key advantage of doubly robust estimators is that they require either the treatment model or the outcome model to be correctly specified to obtain a consistent estimator of average treatment effects, and therefore lead to a more accurate and often more precise inference. However, little work has been done to understand how doubly robust estimators differ due to their unique strategies of using the treatment and outcome models and how machine learning techniques can be combined to boost their performance, which we call double machine learning estimators. Here we examine multiple popular doubly robust methods and compare their performance using different treatment and outcome modeling via extensive simulations and a real-world application. We found that incorporating machine learning with doubly robust estimators such as the targeted maximum likelihood estimator gives the best overall performance. Practical guidance on how to apply doubly robust estimators is provided.