Safety-critical AI applications require causal reasoning under uncertainty. Diagnoses in healthcare, for instance, must not rely on spurious correlations brittle to distributional shifts. Tractable probabilistic modeling (TPM) addresses these needs by providing reliable, efficient inference with guarantees for certified fairness, robustness, and privacy. TPMs are studied across logical reasoning, tensor factorizations, and causal inference, encompassing models with tractable likelihoods (e.g., normalizing flows), tractable marginals (e.g., bounded-treewidth models), and complex tractable reasoning (e.g., probabilistic circuits and tensor networks).

This new edition of TPM builds on the expanding reach of tractable probabilistic models, highlighting recent connections to tensor factorizations and causality and fostering an interchange of ideas between these communities. We welcome contributions on all the TPM spectrum and hope to bring many communities together to help them advance their respective research fields.