A note on the complexity of the causal ordering problem

TL;DR

This paper proves that Simon's classical algorithm for causal ordering is NP-Hard and discusses the computational complexity and potential applications of causal ordering in large-scale systems.

Contribution

It formally establishes the NP-Hardness of Simon's causal ordering algorithm and analyzes the complexity of Nayak's algorithmic solution.

Findings

Simon's causal ordering algorithm is NP-Hard.

Nayak's algorithm runs in bounded time proportional to system size.

Causal ordering has potential in large-scale hypothesis management.

Abstract

In this note we provide a concise report on the complexity of the causal ordering problem, originally introduced by Simon to reason about causal dependencies implicit in systems of mathematical equations. We show that Simon's classical algorithm to infer causal ordering is NP-Hard---an intractability previously guessed but never proven. We present then a detailed account based on Nayak's suggested algorithmic solution (the best available), which is dominated by computing transitive closure---bounded in time by $O(|\mathcal V|\cdot |\mathcal S|)$, where $\mathcal S(\mathcal E, \mathcal V)$ is the input system structure composed of a set $\mathcal E$ of equations over a set $\mathcal V$ of variables with number of variable appearances (density) $|\mathcal S|$. We also comment on the potential of causal ordering for emerging applications in large-scale hypothesis management and analytics.