Isolated Causal Effects of Natural Language

2024-10-18

arXiv.org

AI Summary

This work addresses the modeling and quantification of *isolated causal effects* in natural language—i.e., the independent causal impact of a targeted linguistic intervention (e.g., factual errors) on reader cognition or behavior, while rigorously controlling for confounding influence from non-focal linguistic components. Method: We formally define “language-isolated causal effect” and propose a novel dual-axis evaluation framework grounded in omitted-variable bias theory: one axis measures the fidelity of non-focal language approximation; the other quantifies sensitivity of effect estimation to approximation error. The framework integrates causal inference, controllable language generation, and semi-synthetic data construction. Contribution/Results: Empirical validation on semi-synthetic and real-world datasets demonstrates that our framework accurately recovers ground-truth causal effects and quantitatively characterizes how modeling imperfections in non-focal language systematically bias causal estimates—establishing both theoretical foundations and practical tools for trustworthy causal analysis in NLP.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

As language technologies become widespread, it is important to understand how changes in language affect reader perceptions and behaviors. These relationships may be formalized as the isolated causal effect of some focal language-encoded intervention (e.g., factual inaccuracies) on an external outcome (e.g., readers' beliefs). In this paper, we introduce a formal estimation framework for isolated causal effects of language. We show that a core challenge of estimating isolated effects is the need to approximate all non-focal language outside of the intervention. Drawing on the principle of omitted variable bias, we provide measures for evaluating the quality of both non-focal language approximations and isolated effect estimates themselves. We find that poor approximation of non-focal language can lead to bias in the corresponding isolated effect estimates due to omission of relevant variables, and we show how to assess the sensitivity of effect estimates to such bias along the two key axes of fidelity and overlap. In experiments on semi-synthetic and real-world data, we validate the ability of our framework to correctly recover isolated effects and demonstrate the utility of our proposed measures.

Problem

• Estimating causal effects of language changes on reader perceptions
• Addressing bias from poor non-focal language approximation
• Validating framework for isolated language effect measurement

Innovation

• Formal framework for isolated causal effects
• Measures for non-focal language approximation
• Sensitivity assessment for bias in estimates