# The Generalized Oaxaca-Blinder Estimator

arXiv: https://arxiv.org/abs/2004.11615 · HTML: https://mlsanity.com/paper/2004.11615/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Machine Learning for Variance Reduction in Online Experiments](https://mlsanity.com/paper/2106.07263/) (2021)
- [MEC: Machine-Learning-Assisted Generalized Entropy Calibration for Semi-Supervised Mean Estimation](https://mlsanity.com/paper/2604.05446/) (2026)
- [Fit CATE Once: Model-Assisted Randomization Tests Without Sample Splitting](https://mlsanity.com/paper/2605.09116/) (2026)

## Abstract (arXiv)

After performing a randomized experiment, researchers often use ordinary-least squares (OLS) regression to adjust for baseline covariates when estimating the average treatment effect. It is widely known that the resulting confidence interval is valid even if the linear model is misspecified. In this paper, we generalize that conclusion to covariate adjustment with nonlinear models. We introduce an intuitive way to use any "simple" nonlinear model to construct a covariate-adjusted confidence interval for the average treatment effect. The confidence interval derives its validity from randomization alone, and when nonlinear models fit the data better than linear models, it is narrower than the usual interval from OLS adjustment.