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About this paper
Yiran Jia
An Imputation-Adjusted Environment Invariant (IAEI) estimator is introduced to extend the Environment-Invariant Linear Least Squares (EILLS) framework for scenarios with missing outcome data. This method employs a debiased objective function to account for imputation bias, demonstrating improved variable selection consistency and reduced L2 estimation error in simulations and on a real-world dataset, even with biased imputation models.
Key insights
- A principled bias-correction mechanism effectively integrates imputed data into invariance learning, maintaining consistent variable selection and estimation even when the imputation model itself is biased.
- Leveraging unlabeled data through imputation, when appropriately bias-corrected, enhances the ability to identify true invariant features and improves robust out-of-distribution prediction compared to methods relying solely on observed data.
- Non-asymptotic theoretical guarantees confirm that the IAEI estimator's performance, including variable selection consistency and L2 error convergence rates, is robustly controlled by factors like the proportion of missing data and the magnitude of imputation bias.
Summary by alphaXiv.
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