Accepted Papers

Orals

• Multivariable Causal Discovery with General Nonlinear Relationships
  Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel

• Abstraction between Structural Causal Models: A Review of Definitions and Properties
  Fabio Massimo Zennaro

• Partial Disentanglement via Mechanism Sparsity
  Sebastien Lachapelle, Simon Lacoste-Julien

• Causal Class Activation Maps for Weakly-Supervised Semantic Segmentation
  Yiping Wang

Posters

• A Meta-Reinforcement Learning Algorithm for Causal Discovery
  Andreas W.M. Sauter, Erman Acar, Vincent Francois-Lavet

• Homomorphism Autoencoder — Learning Group Structured Representations from Interactions
  Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F. Grewe, Bernhard Schölkopf

• Function Classes for Identifiable Nonlinear Independent Component Analysis
  Simon Buchholz, Michel Besserve, Bernhard Schölkopf

• Learning Causal Representations with Granger PCA
  Gherardo Varando, Miguel-Ángel Fernández-Torres, Jordi Muñoz-Marí, Gustau Camps-Valls

• Identifiability of deep generative models under mixture priors without auxiliary information
  Bohdan Kivva, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam

• Leveraging Structure Between Environments: Phylogenetic Regularization Incentivizes Disentangled Representations
  Elliot Layne, Dhanya Sridhar, Jason Hartford, Mathieu Blanchette

• Invariance-Based Causal Estimation in the Presence of Concept Drift
  Katie Matton, John Guttag, Rosalind Picard

• Weather2vec: Representation Learning for Causal Inference with Non-Local Confounding in Air Pollution and Climate Studies
  Mauricio Tec, James G. Scott, Corwin Zigler

• Explanatory World Models via Look Ahead Attention for Credit Assignment
  Oriol Corcoll, Raul Vicente

• Selection Collider Bias in Large Language Models
  Emily McMilin

• Towards a Grounded Theory of Causation for Embodied AI
  Taco Cohen

• Probing the Robustness of Independent Mechanism Analysis for Representation Learning
  Joanna Sliwa, Shubhangi Ghosh, Vincent Stimper, Luigi Gresele, Bernhard Schölkopf

• Bias Challenges in Counterfactual Data Augmentation
  S. Chandra Mouli, Yangze Zhou, Bruno Ribeiro

• Towards Computing an Optimal Abstraction for Structural Causal Models
  Fabio Massimo Zennaro, Paolo Turrini, Theo Damoulas

• Structure by Architecture: Disentangled Representations without Regularization
  Felix Leeb, Giulia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer, Bernhard Schölkopf

• SlotFormer: Long-Term Dynamic Modeling in Object-Centric Models
  Ziyi Wu, Nikita Dvornik, Klaus Greff, Jiaqi Xi, Thomas Kipf, Animesh Garg

• Can Large Language Models Distinguish Causal and Anticausal Relations?
  Zhiheng Lyu, Zhijing Jin, Rada Mihalcea, Mrinmaya Sachan, Bernhard Schölkopf

• iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects
  Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves

• Estimating Categorical Counterfactuals via Deep Twin Networks
  Athanasios Vlontzos, Bernhard Kainz, Ciarán Mark Gilligan-Lee

• Intervention Design for Causal Representation Learning
  Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves

• Inductive Biases for Object-Centric Representations in the Presence of Complex Textures
  Samuele Papa, Ole Winther, Andrea Dittadi

• Weakly supervised causal representation learning
  Johann Brehmer, Pim De Haan, Phillip Lippe, Taco Cohen

• Causal Discovery from Conditionally Stationary Time Series
  Carles Balsells Rodas, Ruibo Tu, Yingzhen Li, Hedvig Kjellstrom

• BaCaDI: Bayesian Causal Discovery with Unknown Interventions
  Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, Andreas Krause

• On the DCI Framework for Evaluating Disentangled Representations: Extensions and Connections to Identifiability
  Cian Eastwood, Andrei Liviu Nicolicioiu, Julius Von Kügelgen, Armin Kekic, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf

• GlanceNets: Interpretabile, Leak-proof Concept-based Models
  Emanuele Marconato, Andrea Passerini, Stefano Teso

• Object-based Active Inference
  Ruben van Bergen, Pablo Lanillos

• Can Foundation Models Talk Causality?
  Moritz Willig, Matej Zečević, Devendra Singh Dhami, Kristian Kersting