# An Introduction to Double/Debiased Machine Learning

arXiv: https://arxiv.org/abs/2504.08324 · HTML: https://mlsanity.com/paper/2504.08324/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2025.

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

- [Debiased maximum-likelihood estimators for hazard ratios under kernel-based machine-learning adjustment](https://mlsanity.com/paper/2507.17686/) (2025)
- [A Doubly Robust Machine Learning Approach for Disentangling Treatment Effect Heterogeneity with Functional Outcomes](https://mlsanity.com/paper/2602.11118/) (2026)
- [Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments](https://mlsanity.com/paper/2605.31443/) (2026)
- [Debiased Machine Learning: Identification, Estimation, and Shape Constraints](https://mlsanity.com/paper/2607.24472/) (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)
- [When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation](https://mlsanity.com/paper/2609.00071/) (2026)

## Abstract (arXiv)

This paper provides an introduction to Double/Debiased Machine Learning (DML). DML is a general approach to performing inference about a target parameter in the presence of nuisance functions: objects that are needed to identify the target parameter but are not of primary interest. Nuisance functions arise naturally in many settings, such as when controlling for confounding variables or leveraging instruments. The paper describes two biases that arise from nuisance function estimation and explains how DML alleviates these biases. Consequently, DML allows the use of flexible methods, including machine learning tools, for estimating nuisance functions, reducing the dependence on auxiliary functional form assumptions and enabling the use of complex non-tabular data, such as text or images. We illustrate the application of DML through simulations and empirical examples. We conclude with a discussion of recommended practices. A companion website includes additional examples with code and references to other resources.