# Automatic Debiased Machine Learning for Covariate Shifts

arXiv: https://arxiv.org/abs/2307.04527 · HTML: https://mlsanity.com/paper/2307.04527/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2023.

## Built on by (most cited first, 16 of 16)

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- [Statistical Inference and Learning for Shapley Additive Explanations (SHAP)](https://mlsanity.com/paper/2602.10532/) (2026)
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- [Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias](https://mlsanity.com/paper/2606.14506/) (2026)
- [Causal Machine Learning for Moderation Effects](https://mlsanity.com/paper/2401.08290/) (2024)
- [Predicting Long Term Sequential Policy Value Using Softer Surrogates](https://mlsanity.com/paper/2412.20638/) (2024)
- [Prediction-Powered Causal Inference by Automatic Debiased Machine Learning and Semi-Supervised Riesz Regression](https://mlsanity.com/paper/2606.12892/) (2026)
- [Inferential Evaluation of Surrogate-Derived Models under Covariate Shift](https://mlsanity.com/paper/2608.15783/) (2026)

## Abstract (arXiv)

We present machine learning estimators for causal and predictive parameters under covariate shift, where covariate distributions differ between training and target populations. One such parameter is the average effect of a policy that alters the covariate distribution, such as a treatment modifying surrogate covariates used to predict long-term outcomes. Another example is the average treatment effect for a population with a shifted covariate distribution, like the effect of a policy on the treated group. We propose a debiased machine learning method to estimate a broad class of these parameters in a statistically reliable and automatic manner. Our method eliminates regularization biases arising from the use of machine learning tools in high-dimensional settings, relying solely on the parameter's defining formula. It employs data fusion by combining samples from target and training data to eliminate biases. We prove that our estimator is consistent and asymptotically normal. Computational experiments and an empirical study on the impact of minimum wage increases on teen employment--using the difference-in-differences framework with unconfoundedness--demonstrate the effectiveness of our method.