Age Predictors Through the Lens of Generalization, Bias Mitigation, and Interpretability: Reflections on Causal Implications

TL;DR

This paper explores how invariant representation learning can improve age prediction models' generalization across different groups, emphasizing bias mitigation, interpretability, and causal implications, supported by experiments on mouse transcriptomic data.

Contribution

It introduces a theoretically grounded approach using adversarial neural networks for invariant age prediction and discusses its implications for bias, fairness, and causal analysis.

Findings

Invariant models improve out-of-distribution age prediction.

Adversarial representation learning aligns with causal effects.

Model behavior matches experimental results on mouse data.

Abstract

Chronological age predictors often fail to achieve out-of-distribution (OOD) gen- eralization due to exogenous attributes such as race, gender, or tissue. Learning an invariant representation with respect to those attributes is therefore essential to improve OOD generalization and prevent overly optimistic results. In predic- tive settings, these attributes motivate bias mitigation; in causal analyses, they appear as confounders; and when protected, their suppression leads to fairness. We coherently explore these concepts with theoretical rigor and discuss the scope of an interpretable neural network model based on adversarial representation learning. Using publicly available mouse transcriptomic datasets, we illustrate the behavior of this model relative to conventional machine learning models. We observe that the outcome of this model is consistent with the predictive results of a published study demonstrating the effects of Elamipretide on mouse skeletal and cardiac muscle. We conclude by discussing the limitations of deriving causal interpretation from such purely predictive models.