# hdm: High-Dimensional Metrics

arXiv: https://arxiv.org/abs/1608.00354 · HTML: https://mlsanity.com/paper/1608.00354/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2016.

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

- [DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R](https://mlsanity.com/paper/2103.09603/) (2021)
- [Distributed Bootstrap for Simultaneous Inference Under High Dimensionality](https://mlsanity.com/paper/2102.10080/) (2021)
- [Functional Linear Regression of Cumulative Distribution Functions](https://mlsanity.com/paper/2205.14545/) (2022)
- [Convolutional neural networks for valid and efficient causal inference](https://mlsanity.com/paper/2301.11732/) (2023)
- [Valid causal inference with unobserved confounding in high-dimensional settings](https://mlsanity.com/paper/2401.06564/) (2024)
- [Estimation in high-dimensional linear regression: Post-Double-Autometrics as an alternative to Post-Double-Lasso](https://mlsanity.com/paper/2511.21257/) (2025)

## Abstract (arXiv)

In this article the package High-dimensional Metrics (texttthdm) is introduced. It is a collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for (possibly many) low-dimensional subcomponents of the high-dimensional parameter vector. Efficient estimators and uniformly valid confidence intervals for regression coefficients on target variables (e.g., treatment or policy variable) in a high-dimensional approximately sparse regression model, for average treatment effect (ATE) and average treatment effect for the treated (ATET), as well for extensions of these parameters to the endogenous setting are provided. Theory grounded, data-driven methods for selecting the penalization parameter in Lasso regressions under heteroscedastic and non-Gaussian errors are implemented. Moreover, joint/ simultaneous confidence intervals for regression coefficients of a high-dimensional sparse regression are implemented. Data sets which have been used in the literature and might be useful for classroom demonstration and for testing new estimators are included.