# Estimation in high-dimensional linear regression: Post-Double-Autometrics as an alternative to Post-Double-Lasso

arXiv: https://arxiv.org/abs/2511.21257 · HTML: https://mlsanity.com/paper/2511.21257/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 26 Nov 2025.

## Builds on (most cited first, 2 of 2)

- [hdm: High-Dimensional Metrics](https://mlsanity.com/paper/1608.00354/) (2016)
- [Inference in Linear Regression Models with Many Covariates and Heteroskedasticity](https://mlsanity.com/paper/1507.02493/) (2015)

## Abstract (arXiv)

Post-Double-Lasso is becoming the most popular method for estimating linear regression models with many covariates when the purpose is to obtain an accurate estimate of a parameter of interest, such as an average treatment effect. However, this method can suffer from substantial omitted variable bias in finite sample. We propose a new method called Post-Double-Autometrics, which is based on Autometrics, and show that this method outperforms Post-Double-Lasso. Its use in a standard application of economic growth sheds new light on the hypothesis of convergence from poor to rich economies.