Causal Fairness for Survival Analysis

Authors: Drago Plecko

arXiv · PDF

Summary

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.

Relevance

Not directly related to my persona/midtraining work — this is about fairness in survival analysis for medical/social applications, not about language model behavior or persona installation.

Threat model

Potential threat/caveat for clean result "Fine-tuning one persona on a two-marker chunk and another on the start marker plants the end marker at every donor answer's end, not chained to the start (LOW confidence)": this item discusses bias, confound.

Abstract

arXiv:2605.11362v1 Announce Type: cross Abstract: In the data-driven era, large-scale datasets are routinely collected and analyzed using machine learning (ML) and artificial intelligence (AI) to inform decisions in high-stakes domains such as healthcare, employment, and criminal justice, raising concerns about the fairness behavior of these systems. Existing works in fair ML cover tasks such as bias detection, fair prediction, and fair decision-making, but largely focus on static settings. At the same time, fairness in temporal contexts, particularly survival/time-to-event (TTE) analysis, remains relatively underexplored, with current approaches to fair survival analysis adopting statistical fairness definitions, which, even with unlimited data, cannot disentangle the causal mechanisms that generate disparities. To address this gap, we develop a causal framework for fairness in TTE analysis, enabling the decomposition of disparities in survival into contributions from direct, indirect, and spurious pathways. This provides a human-understandable explanation of why disparities arise and how they evolve over time. Our non-parametric approach proceeds in four steps: (1) formalizing the necessary assumptions about censoring and lack of confounding using a graphical model; (2) recovering the conditional survival function given covariates; (3) applying the Causal Reduction Theorem to reframe the problem in a form amenable to causal pathway decomposition; (4) estimating the effects efficiently. Finally, our approach is used to analyze the temporal evolution of racial disparities in outcome after admission to an intensive care unit (ICU).