The authors develop a causal framework for fairness in survival/time-to-event analysis, moving beyond statistical fairness definitions to decompose disparities in survival into direct, indirect, and spurious causal pathways. Their non-parametric approach recovers conditional survival functions, applies the "Causal Reduction Theorem" to enable pathway decomposition, and estimates effects efficiently, providing human-understandable explanations of why disparities arise and evolve over time.
Main takeaways:
- Existing fair ML work on survival analysis uses statistical fairness definitions that can't disentangle causal mechanisms even with unlimited data.
- The framework decomposes survival disparities into direct, indirect, and spurious pathways, explaining why disparities arise.
- Proceeds in four steps: formalizing censoring/confounding assumptions graphically, recovering conditional survival functions, applying the Causal Reduction Theorem, and estimating effects.
- Applied to analyze racial disparities in ICU outcomes over time.
- Provides temporal evolution of disparities, not just static snapshots.