# Estimating Causal Effects with Double Machine Learning -- A Method Evaluation

arXiv: https://arxiv.org/abs/2403.14385 · HTML: https://mlsanity.com/paper/2403.14385/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 85 references that are in the map.
- First posted on arXiv 21 Mar 2024.

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

- [Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters](https://mlsanity.com/paper/2408.09598/) (2024)
- [Do we actually understand the impact of renewables on electricity prices? A causal inference approach](https://mlsanity.com/paper/2501.10423/) (2025)
- [Practical programming research of Linear DML model based on the simplest Python code: From the standpoint of novice researchers](https://mlsanity.com/paper/2502.16172/) (2025)
- [Heatwave increases nighttime light intensity in hyperdense cities of the Global South: A double machine learning study](https://mlsanity.com/paper/2503.00557/) (2025)
- [Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality](https://mlsanity.com/paper/2605.01907/) (2026)
- [Analytical and Bootstrap Confidence Intervals for Double Machine Learning: Simulation Studies and an Application to Rural-Urban Differences in Obesity Prevalence](https://mlsanity.com/paper/2607.29456/) (2026)

## Abstract (arXiv)

The estimation of causal effects with observational data continues to be a very active research area. In recent years, researchers have developed new frameworks which use machine learning to relax classical assumptions necessary for the estimation of causal effects. In this paper, we review one of the most prominent methods - "double/debiased machine learning" (DML) - and empirically evaluate it by comparing its performance on simulated data relative to more traditional statistical methods, before applying it to real-world data. Our findings indicate that the application of a suitably flexible machine learning algorithm within DML improves the adjustment for various nonlinear confounding relationships. This advantage enables a departure from traditional functional form assumptions typically necessary in causal effect estimation. However, we demonstrate that the method continues to critically depend on standard assumptions about causal structure and identification. When estimating the effects of air pollution on housing prices in our application, we find that DML estimates are consistently larger than estimates of less flexible methods. From our overall results, we provide actionable recommendations for specific choices researchers must make when applying DML in practice.