This winter, the Big Statistics group of the Epidemiology and Data Science department, Amsterdam University Medical Centers, will host a single-day workshop on explainable machine learning (xML), featuring renowned international speakers.

Date: February 4th 2026
Time: 9:30 – 16:30 (CEST)
Location: IJ-zaal of RDC ADORE building, Van der Boechorststraat 6B, 1007 MB Amsterdam
Registration deadline: January 15th 2026
Maximum capacity: 60 people
Cost: free
Description:
Complex machine learning models are increasingly applied to medical data because of their strong predictive performance and ability to capture interactions and nonlinear relationships. However, their complexity makes it difficult to gain insight in the application at hand. Explainable machine learning (xML) provides a framework of techniques that help researchers (1) understand how models arrive at their predictions and (2) identify which variables are important.

In health care, explainability is particularly relevant. Insight into why predictions are made may generate new biomedical understanding and may ultimately support clinical decision-making. However, the typically small sample sizes in medical studies pose challenges for both model fitting and interpretation.

This workshop is intended to researchers with a methodological/statistical background who are interested in applying state-of-the-art explainable ML methods to health-related data. The program features a mix of theory and applications, with a particular focus on Shapley values and their recent methodological developments. Other xML approaches will also be discussed.

Provisional programme:

09.00h – 09.30h Coffee + Welcome
09.30h – 10.05h Marjolein Fokkema (Leiden University). Shapley values with Bayesian uncertainty quantification for rule ensembles 
10.05h – 10.30h Jeroen Goedhart (Amsterdam UMC). Asymmetric Shapley values to quantify the importance of genes in clinico-genomic applications 
10.30h – 10.55h Angel Reyero Lobo (Institut de Mathématiques de Toulouse). A principled approach for comparing Variable Importance 
10.55h – 11.15h Break
11.15h – 11.50h Ioan Gabriel Bucur (Radboud University Nijmegen). A causal perspective on Shapley values 
11.50h – 12.25h Konstantinos Sechidis (Novartis, Basel). Using Explainable Machine Learning for Assessing Treatment Effect Heterogeneity in Clinical Trials
12.25h – 13.55h Lunch (provided)
13.55h – 14.20h Marvin Wright (Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen). Functional Decomposition of Tree-Based Models
13.20h – 14.45h Sophie Langbein (Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen).  Interpretable Machine Learning for Survival Analysis
14.45h – 15.30h Martin Jullum (Norwegian Computing Center, Oslo). Software demo: shapr – Conditional Shapley Value Explanation in R and Python
15.30h – 15.50h Break
15.50h – 16.25h Giovanni Cinà (Amsterdam UMC). Is my model perplexed for the right reason? Contrasting LLMs’ Benchmark Behavior with Behavior Specified via Token-Level Perplexity
16.25h – 17.00h Kjersti Aas (Norwegian Computing Center, Oslo). MCCE: Monte Carlo sampling of realistic counterfactual explanations

Specific topics include:
– Shapley values for causal interpretations
– xML methods for tree-based learners and survival analysis
– Computational aspects of Shapley values
– Applications in omics

Organizing Committee: Mark van de Wiel [mark.vdwiel@amsterdamumc.nl], Giorgio Spadaccini [g.spadaccini@amsterdamumc.nl], and Jeroen Goedhart [j.m.goedhart@amsterdamumc.nl]

Sponsors:

-Amsterdam Public Health research institute (APH)
-VVSOR Biometrics / IBS Dutch region (BMS-ANed)
-Dep. Epidemiology & Data Science of Amsterdam UMC

Registration: