Causality and Semantic Separation

TL;DR

This paper rigorously formalizes the concept of d-separation in causal graphs using a semantic approach inspired by security noninterference, providing a foundational understanding of its correctness independent of probabilistic assumptions.

Contribution

It introduces a semantic characterization of d-separation, mechanized in Rocq, linking graph conditions to noninterference-inspired semantics for causal inference.

Findings

d-separation coincides with a novel semantic definition

Provides a structural semantic foundation for d-separation

Justifies methods for falsifying experiment world-models

Abstract

The design of scientific experiments deserves its own variation of formal verification to catch cases where scientists made important mistakes, such as forgetting to take confounding variables into account. One of the most fundamental underpinnings of science is causality, or what it means for interventions in the world to cause other outcomes, as formalized by computer scientists like Judea Pearl. However, these ideas had not previously been made rigorous to the standards of the programming-languages community, where one expects a (syntactic) program analysis to be proved sound with respect to a natural semantics. In the domain of causality, as the relevant "program analysis," we focus on $d$-separation, a classic condition on graphs that can be used to decide when the design of an experiment controls for sufficiently many confounding variables, even though the reason that this condition works is often unintuitive. Our central result (mechanized in Rocq) is that $d$-separation exactly coincides with a novel semantic definition inspired by noninterference from the theory of security. This characterization provides a structural semantic foundation for $d$-separation and helps explain why the graph-theoretic condition is correct, independently of probabilistic assumptions. For each given automated test on the quality of an experiment design, our theorem justifies an associated method for falsifying the world-modeling hypothesis behind the experiment.