Evaluating Bivariate Causal Statements Based on Mutual Compatibility
Quick Take
This study introduces methods to evaluate bivariate causal statements among n variables, focusing on compatibility and incompatibility scores that assess plausibility without faithfulness assumptions. The approach successfully distinguishes correct from incorrect causal claims, particularly in analyzing assertions made by large language models.
Key Points
- Develops compatibility and incompatibility scores for evaluating causal statements.
- Scores can distinguish correct from incorrect causal claims in various settings.
- Methodology applies to analyzing causal claims from large language models.
- Focuses on acyclic linear statements and avoids faithfulness assumptions.
- Provides a foundation for assessing causal information reliability.
Article Content
From source RSS / original summaryarXiv:2606. 00278v1 Announce Type: new Abstract: For many real-world systems, causal ground truth is difficult to obtain, making claims about causal effects hard to assess. We develop methods for evaluating collections of $\binom{n}{2}$ bivariate causal statements over a set of $n$ variables.
In the setting of acyclic linear statements, any such collection can be extended to a unique multivariate causal model, but we argue that this induced model is implausible if it imposes substantial additional confounding to explain observed correlations. We introduce a compatibility score that quantifies this notion of plausibility, notably without relying on the faithfulness assumption.
Additionally, we define an incompatibility score for purely graphical bivariate causal statements, based on global consistency constraints that are derived from acyclicity and faithfulness assumptions. We give theoretical and empirical evidence that both scores can successfully distinguish correct from incorrect causal statements in generic settings. Moreover, we demonstrate the practical applicability of our methods by analyzing causal claims made by large language models.
Our work aims to provide a foundation for assessing the reliability of causal information derived from human experts or artificial intelligence in settings where alternative forms of validation are unavailable.
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