On probabilistic and causal reasoning with summation operators

Abstract Ibeling et al. (2023, Annals of Pure and Applied Logic, 103339) axiomatize increasingly expressive languages of causation and probability, and Mossé et al. (2024, The Review of Symbolic Logic, 17, 106–131) show that reasoning (specifically the satisfiability problem) in each causal language is as difficult, from a computational complexity perspective, as reasoning in its merely probabilistic or ‘correlational’ counterpart. Introducing a summation operator to capture common devices that appear in applications—such as the $do$-calculus of Pearl (Causality. Cambridge University Press, 2009) for causal inference, which makes ample use of marginalization—van der Zander et al. (The hardness of reasoning about probabilities and causality. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, E. Elkind, ed., pp. 5730–5738. International Joint Conferences on Artificial Intelligence Organization. Main Track. 2023) extend these earlier complexity results to causal and probabilistic languages with marginalization. We complete this extension, fully characterizing the complexity of probabilistic and causal reasoning with summation, demonstrating that these again remain equally difficult. Surprisingly, allowing free variables for random variable values results in a system that is undecidable, so long as the ranges of these random variables are unrestricted. We finally axiomatize these languages featuring marginalization (or more generally summation), resolving open questions posed by Ibeling et al. (2023, Annals of Pure and Applied Logic, 103339).