Learning Invariant Representations with Missing Data.

Area of Science:

• Machine Learning
• Causal Inference
• Data Science

Background:

• Spurious correlations in machine learning models lead to poor generalization on test populations despite good training performance.
• Invariance principles, enforcing independencies with nuisance variables, offer theoretical guarantees for model test performance.
• Nuisance variables (e.g., demographics, background labels) are often unobserved during training, limiting the application of these guarantees.

Purpose of the Study:

• To develop methods for enforcing independencies with missing nuisance variables in machine learning.
• To derive Maximum Mean Discrepancy (MMD) estimators for invariance objectives when nuisance data is incomplete.
• To evaluate the effectiveness of these estimators on simulated and real-world clinical data.

Main Methods:

• Derivation of novel MMD estimators tailored for invariance objectives under missing nuisance data.
• Optimization of models using these derived MMD estimators.
• Empirical validation on simulated datasets and clinical data to assess performance.

Main Results:

• The proposed MMD estimators effectively handle missing nuisance variables.
• Optimizing through these estimates yields test performance comparable to methods using complete nuisance data.
• Demonstrated robustness and applicability in both simulated environments and clinical settings.

Conclusions:

• The developed MMD estimators provide a practical solution for achieving model invariance with incomplete nuisance information.
• This approach enhances model generalization and reliability in real-world scenarios where data is often partially missing.
• The findings suggest a viable path towards more robust and trustworthy machine learning applications, particularly in sensitive domains like healthcare.