Title: Assessing Bivariate Causal Assertions Through Mutual Consistency

Abstract

Obtaining definitive causal ground truth is often a significant challenge in real-world systems, thereby complicating the evaluation of causal effect claims. This study presents methodologies for scrutinizing collections of $\binom{n}{2}$ bivariate causal statements concerning a set of $n$ variables. Within the framework of acyclic linear statements, any given collection can theoretically be extended to form a unique multivariate causal model. However, we contend that such an induced model lacks plausibility if it necessitates substantial additional confounding to account for observed correlations. To address this, we propose a compatibility score that measures this plausibility metric, notably without depending on the faithfulness assumption. Furthermore, for purely graphical bivariate causal statements, we define an incompatibility score grounded in global consistency constraints derived from both acyclicity and faithfulness assumptions. We provide both theoretical and empirical support demonstrating that these scores effectively differentiate between accurate and erroneous causal statements in general contexts. Additionally, we illustrate the practical utility of our approach by examining causal assertions generated by large language models. Ultimately, this research seeks to establish a basis for judging the reliability of causal insights obtained from either human specialists or artificial intelligence, particularly in scenarios where other validation methods are inaccessible.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC