# Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data

arXiv: https://arxiv.org/abs/2308.11446 · HTML: https://mlsanity.com/paper/2308.11446/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 14 of 14)

- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models](https://mlsanity.com/paper/1612.08468/) (2016)
- [On the Existence of Simpler Machine Learning Models](https://mlsanity.com/paper/1908.01755/) (2019)
- [Exploring the Whole Rashomon Set of Sparse Decision Trees](https://mlsanity.com/paper/2209.08040/) (2022)
- [Rashomon Capacity: A Metric for Predictive Multiplicity in Classification](https://mlsanity.com/paper/2206.01295/) (2022)
- [The Grammar of Interactive Explanatory Model Analysis](https://mlsanity.com/paper/2005.00497/) (2020)
- [Robust Optimization using Machine Learning for Uncertainty Sets](https://mlsanity.com/paper/1407.1097/) (2014)
- [Forward Stability and Model Path Selection](https://mlsanity.com/paper/2103.03462/) (2021)
- [Performance is not enough: the story told by a Rashomon quartet](https://mlsanity.com/paper/2302.13356/) (2023)
- [Identifying a minimal class of models for high-dimensional data](https://mlsanity.com/paper/1504.00494/) (2015)
- [SeFNet: Bridging Tabular Datasets with Semantic Feature Nets](https://mlsanity.com/paper/2306.11636/) (2023)

## Built on by (most cited first, 11 of 11)

- [Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning](https://mlsanity.com/paper/2501.14959/) (2025)
- [Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time](https://mlsanity.com/paper/2503.01087/) (2025)
- [SORTeD Rashomon Sets of Sparse Decision Trees: Anytime Enumeration](https://mlsanity.com/paper/2511.03344/) (2025)
- [ExplainerPFN: Towards tabular foundation models for model-free zero-shot feature importance estimations](https://mlsanity.com/paper/2601.23068/) (2026)
- [On Arbitrary Predictions from Equally Valid Models](https://mlsanity.com/paper/2507.19408/) (2025)
- [Complete Evidence Extraction with Model Ensembles: A Case Study on Medical Coding](https://mlsanity.com/paper/2511.07055/) (2025)
- [Resolving Predictive Multiplicity for the Rashomon Set](https://mlsanity.com/paper/2601.09071/) (2026)
- [CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameters](https://mlsanity.com/paper/2603.15321/) (2026)
- [From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detection](https://mlsanity.com/paper/2412.11308/) (2024)
- [Efficient & Correct Predictive Equivalence for Decision Trees](https://mlsanity.com/paper/2509.17774/) (2025)
- [An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness](https://mlsanity.com/paper/2604.23954/) (2026)

## Abstract (arXiv)

The machine learning modeling process conventionally culminates in selecting a single model that maximizes a selected performance metric. However, this approach leads to abandoning a more profound analysis of slightly inferior models. Particularly in medical and healthcare studies, where the objective extends beyond predictions to valuable insight generation, relying solely on a single model can result in misleading or incomplete conclusions. This problem is particularly pertinent when dealing with a set of models known as textitRashomon set, with performance close to maximum one. Such a set can be numerous and may contain models describing the data in a different way, which calls for comprehensive analysis. This paper introduces a novel process to explore models in the Rashomon set, extending the conventional modeling approach. We propose the textttRashomonDETECT algorithm to detect models with different behavior. It is based on recent developments in the eXplainable Artificial Intelligence (XAI) field. To quantify differences in variable effects among models, we introduce the Profile Disparity Index (PDI) based on measures from functional data analysis. To illustrate the effectiveness of our approach, we showcase its application in predicting survival among hemophagocytic lymphohistiocytosis (HLH) patients - a foundational case study. Additionally, we benchmark our approach on other medical data sets, demonstrating its versatility and utility in various contexts. If differently behaving models are detected in the Rashomon set, their combined analysis leads to more trustworthy conclusions, which is of vital importance for high-stakes applications such as medical applications.