On Probabilistic and Causal Reasoning with Summation Operators

TL;DR

This paper fully characterizes the computational complexity of probabilistic and causal reasoning with summation operators, revealing that such reasoning remains as difficult as prior models and becomes undecidable with unrestricted variables.

Contribution

It completes the complexity analysis of causal and probabilistic languages with summation, and axiomatizes these languages, addressing open questions from previous work.

Findings

Reasoning with summation operators is computationally as hard as previous models.

Allowing unrestricted free variables leads to undecidability.

The paper provides a complete axiomatization of languages with marginalization.

Abstract

Ibeling et al. (2023). axiomatize increasingly expressive languages of causation and probability, and Mosse et al. (2024) 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 (2009) for causal inference, which makes ample use of marginalization -- van der Zander et al. (2023) partially 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).