Pearl’s causal hierarchy defines three levels of problems in data-driven AI: (1) observation, (2) intervention, and (3) counterfactuals.  Problems at higher levels of the hierarchy are more challenging to solve, as they require a deeper understanding of causality that cannot be learned from passively observed data alone. Regarding explainability, these problems and their solutions provide new challenges and opportunities for cross-fertilization with causal studies. In this special track, we focus on these challenges and opportunities. We are interested in the inner causality of models (such as mechanistic interpretability) and external causality (relates to how the model fits with its environment), both of which should enable safety and security analysis. Furthermore, inner causality should enable recognizing and replacing parts of models with coded modules that increase reliability and performance.


Causal regularization techniques for xAI
Contrastive, interventional, backtracking, diffeomorphic counterfactual explanations
Inner causal explanations with mechanistic interpretability and concept-based learning
Causal Discovery with representation learning
Generative AI as preprocessing for xAI
Exploring causality with deep generative models
Generative flow networks for explainability
Normalizing flow networks for explainable artificial intelligence
Observational and interventional causal approaches to XAI
Explaining optimization solutions for causal decision intelligence in XAI
Explaining sources of infeasibility in optimization models
Explainability via amortized optimization models and fitness landscapes
Causal transportability
Digital twins and simulators for interpretable synthetic data generation
Graph neural network causal learning
Causal world models, distillation, importance in causal-explainable reinforcement learning
Interpretable/Explainable root cause analysis methods
Explainable AI with active inference techniques
Algorithmic information theory and algorithmic information dynamics for causal discovery
XAI methods for causality in spatiotemporal forecasting
Interpretable Causal Discovery via affective computing and consciousness studies
Observational and Interventional Causal Approaches to XAI
Causality and XAI in high-stakes decision-making
Topological data analysis and Information geometry for relations between explainability and causality
XAI in scientific discovery of mechanistic model
Optimal transport for counterfactual models