# Aligning Data-Driven Predictors with Allocation: A Decision-Focused Approach to Survival Analysis

arXiv: https://arxiv.org/abs/2606.02671 · HTML: https://mlsanity.com/paper/2606.02671/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 60 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Builds on (most cited first, 21 of 21)

- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization](https://mlsanity.com/paper/1809.05504/) (2018)
- [Algorithms with Predictions](https://mlsanity.com/paper/2006.09123/) (2020)
- [Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities](https://mlsanity.com/paper/2307.13565/) (2023)
- [A Theoretical Analysis of NDCG Type Ranking Measures](https://mlsanity.com/paper/1304.6480/) (2013)
- [Calibration in Deep Learning: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2308.01222/) (2023)
- [Deep Cox Mixtures for Survival Regression](https://mlsanity.com/paper/2101.06536/) (2021)
- [Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters](https://mlsanity.com/paper/2104.04103/) (2021)
- [Treatment Effect Estimation for Optimal Decision-Making](https://mlsanity.com/paper/2505.13092/) (2025)
- [Algorithms with Calibrated Machine Learning Predictions](https://mlsanity.com/paper/2502.02861/) (2025)
- [Position-Indexed Formulations for Kidney Exchange](https://mlsanity.com/paper/1606.01623/) (2016)
- [Policy Optimization for Dynamic Heart Transplant Allocation](https://mlsanity.com/paper/2512.12497/) (2025)
- [Near-Optimal Dynamic Matching via Coarsening with Application to Heart Transplantation](https://mlsanity.com/paper/2602.04989/) (2026)
- [Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives](https://mlsanity.com/paper/2602.04990/) (2026)
- [Metalearners for Ranking Treatment Effects](https://mlsanity.com/paper/2405.02183/) (2024)
- [Rank-Learner: Orthogonal Ranking of Treatment Effects](https://mlsanity.com/paper/2602.03517/) (2026)
- [Learning Potentials for Dynamic Matching and Application to Heart Transplantation](https://mlsanity.com/paper/2602.08878/) (2026)
- [Adaptively Weighted Top-N Recommendation for Organ Matching](https://mlsanity.com/paper/2107.10971/) (2021)
- [Necessary Optimality Conditions for Integrated Learning and Optimization Problem in Contextual Optimization](https://mlsanity.com/paper/2601.16581/) (2026)

## Built on by (most cited first, 2 of 2)

- [Preference Elicitation for Policy Optimization and Application to Aligning Heart Transplantation with Human Values](https://mlsanity.com/paper/2608.28620/) (2026)
- [Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring](https://mlsanity.com/paper/2610.12538/) (2026)

## Abstract (arXiv)

Machine learning predictors have become essential tools for guiding automated decision making. However, a major misalignment persists: predictive models are typically optimized in terms of standard statistical metrics in isolation from the algorithmic tasks they inform. We highlight this incongruity in the high-stakes domain of organ allocation by demonstrating that any algorithm relying on (even highly accurate) survival predictors optimized for standard metrics -- such as the Concordance index (C-index) -- can yield arbitrarily poor outcomes when used for allocation, failing to guarantee utility better than a uniform random selection. To bridge the gap between survival analysis and policy optimization, we introduce a decision-focused learning approach based on optimizing normalized discounted cumulative gain (NDCG), a mainstay metric in information retrieval. We establish the utility of NDCG in survival analysis by proving that it translates to guarantees on the performance of allocation. Empirically, we propose a bootstrapping approach to optimize the NDCG of existing survival models. Unlike prior work, we also address the challenge of right censorship when evaluating ranking. On historical heart transplant data from the US, our method dramatically boosts the NDCG of baseline models by 50-100%, which translates to tens of thousands of additional life years gained annually when deployed for transplant allocation. We anticipate that our framework will find broader applications in decision making with predictions.