# ddml: Double/debiased machine learning in Stata

arXiv: https://arxiv.org/abs/2301.09397 · HTML: https://mlsanity.com/paper/2301.09397/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 67 references that are in the map.
- First posted on arXiv 23 Jan 2023.

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

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Nonparametric regression using deep neural networks with ReLU activation function](https://mlsanity.com/paper/1708.06633/) (2017)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Program Evaluation and Causal Inference with High-Dimensional Data](https://mlsanity.com/paper/1311.2645/) (2013)
- [High-Dimensional L2Boosting: Rate of Convergence](https://mlsanity.com/paper/1602.08927/) (2016)
- [High-Dimensional Metrics in R](https://mlsanity.com/paper/1603.01700/) (2016)
- [pystacked: Stacking generalization and machine learning in Stata](https://mlsanity.com/paper/2208.10896/) (2022)

## Built on by (most cited first, 1 of 1)

- [Detecting clinician implicit biases in diagnoses using proximal causal inference](https://mlsanity.com/paper/2501.16399/) (2025)

## Abstract (arXiv)

We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using DDML in combination with stacking estimation which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation.