Probabilistic Graphical Models 2026 - Program

Causal Reasoning in the Earth System: Progress, Pitfalls, and Open Problems

Applied causal machine learning for agriculture and food systems

Extremal Graphical Models

• Semiparametric Inference for Half-Trek Estimators in Linear Structural Equation Models
  Leopold Mareis, Nils Sturma, Mathias Drton
• Partial Identification under Causal Orders by Linear Programming
  Eric Rossetto, Alessandro Antonucci
• Partially Ordering Graphical Models with Latent Confounders by their Equality Constraints
  Thijs van Ommen
• I-FLOP: Fast Learning of Order and Parents from Interventional Data
  Liuting Chen, Alex Markham
• An AutoML-Powered Architecture for Causal Discovery
  Konstantina Biza, Sofia Triantafillou, Ioannis Tsamardinos

• Federated Causal Discovery via Regression-Directed Cumulants
  Pablo Torrijos, Fabio Stella, José A. Gámez, Jose M. Puerta
• Fast Constraint-Based Structure Learning for Continuous-Time Bayesian Networks
  Alessandro Bregoli, Marco Midali, Marco Scutari, Fabio Stella, Alessio Zanga
• Bayesian Network MPE via LLM-Compiled Semantic Maximizing Circuits
  Victor Hugo de Souza Singulani Ragazzi, Jhonatan Oliveira, Cory Butz, Leonardo Bonato Felix

• Differentiable Structural EM for Explicit-Latent Linear-Gaussian DAGs
  Mohammad Ali Javidian, R. Mitchell Parry
• Tensor Train Decomposition for BN2A Structure Learning
  Iván Pérez, Jirka Vomlel, Patrícia Martinková
• Algorithmics for Graphical Separation Criteria
  Moisés Chavira-Flores, Sebastian Weichwald, Leonard Henckel

• Predicting the Risk of Vehicle-Cyclist Crashes in Urban Intersections Using Bayesian Networks
  Kranthi Kumar Talluri, Anders Madsen, Galia Weidl, Christer Ahlström, Johan Olstam
• LLM-Augmented Causal Discovery: Probabilistic Fusion of Edge Existence and Orientation
  Neville Kenneth Kitson, Anthony Constantinou
• BNqMark: Bayesian-Network Inference in Large Language Models
  Fernando Rodriguez, Bojan Mihaljević
• Testable Implications of Causal Graphs in the Presence of Measurement Error
  Karthika Mohan, Samuel Zink
• Hierarchically Coupled Gaussian Bayesian Networks
  Marco Grzegorczyk
• Selective Indicator Elimination in Arithmetic Circuits for Bipartite Noisy-OR Bayesian Networks
  Cory Butz, Alejandro Santoscoy-Rivero, Jirka Vomlel, Anders Madsen
• A Bayesian Network Approach to Earth Fault Localisation in MV Energy Grids
  Anders Madsen, Somesh Bhattacharya, Hans-Peter Schwefel, Rasmus L Olsen
• Gradient-Based Fine-Tuning of Expert Bayesian Network Classifiers: Application in Forensic Porcine Wound Age Assessment
  Joseph Mietkiewicz, Anders Madsen, Thomas Dyhre Nielsen, Cecilie Bækgård, Henrik Elvang Jensen, Kristiane Barington
• Using P&ID and Process Data To Construct Bayesian Networks For Anomaly Detection
  Jakob Ø. Hansen, Anders Madsen, Thomas Dyhre Nielsen, Joseph Mietkiewicz, Gabriele Baldissone, Micaela Demichela
• Table-based Probability Trees: A New Hybrid Potential Representation for Probabilistic Inference in Graphical Models
  Francisco Bonillo, Andres Cano, Manuel Gomez-Olmedo, Serafín Moral
• Inclusion-Driven Learning from Interventional Data with the R Package idlBNs
  Robert Castelo
• Scope-Restricted Backtracking Counterfactuals
  Johan de Aguas, Martin Jullum

Bayesian Computation for High-Dimensional Gaussian Graphical Models with Spike-and-Slab Priors

• On Computing Total Variation Distance between Markov Logic Networks
  Zixiang Xiong, Peter Jung, Qiaolan Meng, Yuanhong Wang, Ondrej Kuzelka, Yuyi Wang
• Safe Inverse Marginalization in Bayesian Networks
  Cory Butz, Alejandro Santoscoy-Rivero, Silja Renooij, Johan Kwisthout
• Shapeshifter: A Dynamic Graph-Restructuring Algorithm for Improved Inference in Loopy-Structured PGMs
  Katerine Marí Sadie, Johan Adam du Preez, Willie Brink
• Optimized Priority Scheduling for Faster Scalable Belief Propagation
  Abnash Bassi, Gilead Posluns, Mark C. Jeffrey
• On the Detection of Commutative Factors in Factor Graphs: Necessary and Sufficient Conditions
  Malte Luttermann, Ralf Möller, Marcel Gehrke

• Accounting for Data Uncertainty in Counterfactual Reasoning
  Rafael Cabañas, Helge Lagseth, Thomas Dyhre Nielsen, Antonio Salmeron
• Learning Causal Structure of Time Series using Best Order Score Search
  Irene Gema Castillo Mansilla, Urmi Ninad
• Bayesian Estimation of Causal Effects in Bayesian Networks with Local Structure
  Vera Kvisgaard, Johan Pensar
• Counterfactual Fairness under Hidden Confounding
  Marc Braun, Jose Peña, Adel Daoud

• A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression
  Wouter W. L. Nuijten, Albert Podusenko, Ismail Senoz, Esther G. van Pelt, Wouter M. Kouw
• Forward Sampling for State Variable Graphical Event Models with Markovian and Piecewise Constant Dependencies
  Moustafa Said Hawchar, Philippe Leray, Yanis Ouerdani, Pierre Jannin
• Augmenting Bayesian Networks with Categorical Principal Components
  David Bojko, Marie Melínová, Jirka Vomlel
• A Comparative Review of Probabilistic Inference for Timescale Graphical Event Models
  El Mokhtar Hribach, Julien Blanchard, Philippe Leray
• LOCO-FL: Scaling and Reference Choice for Bayesian Structural Analysis of Between-Center Heterogeneity in Federated Learning with Markov Boundaries
  András Millinghoffer, Júlia Lili Kisida, Andrea Valek, Miklós Szabó, Peter Antal
• Causal Modelling of Support Interventions in Student Competency Assessment
  Francesca Mangili, Rafael Cabañas, Alessandro Antonucci
• Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: A General Approach
  Adrián Detavernier, Jasper De Bock
• Finite Dimensional Approximations of Incentive Functions for Causal Incentive Design Models
  Sebastián Bejos, Eduardo F. Morales, Enrique Munoz de Cote, L. Enrique Sucar
• What to Check Next? Bayesian Networks and Off-Policy Expected Returns for Quicker Inspections
  Sebastiaan J. J. Jans, Thijs van Ommen, Ad Feelders, Cor J. Veenman
• Non-Symmetric Information Operators in GMRFs for PDE Inference
  Yumeng Shi, Concha Bielza, Pedro Larrañaga
• Bayesium: An Agentic RAG Interface for Traceable Exploration of Bayesian Network Literature
  Kevin Paniagua, Adrián Raposo, Concha Bielza, Pedro Larrañaga

Understanding Regional Climate Variability through Causal Data Science and Machine Learning

• Parameter Sensitivity Analysis in Mixtures of Polynomials
  Ángel T. Sáez-Ruiz, Rafael Rumi, Ana D. Maldonado
• Neuro-Causal Factor Analysis
  Alex Markham, Mingyu Liu, Bryon Aragam, Liam Solus
• How Does Bayesian Causal Discovery Fail: Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding
  Debargha Ghosh, Silja Renooij, Anna V. Kononova
• Spectral Sparsification of Laplacian-Constrained Gaussian and Hüsler–Reiss Graphical Models
  Ignacio Echave-Sustaeta Rodríguez, Aida Abiad Monge, Frank Röttger
• Parameterising Gaussian Graphical Models
  Jack Storror Carter