Counterfactual Reasoning for Retrieval-Augmented Generation
Huaiyu Qin, Chunyu Wei, Yueguo Chen, Yunhai Wang
Abstract
While Retrieval-Augmented Generation (RAG) has advanced knowledge-intensive tasks, we identify a fundamental vulnerability: the Correlation Trap. Existing systems cannot distinguish causally decisive evidence from overwhelmingly correlated yet misleading information, leading to systematic failures. We introduce Counterfactual RAG (CF-RAG), a new framework that operationalizes causal reasoning to overcome this limitation. CF-RAG systematically generates and evaluates counterfactual queries to identify causally relevant distinctions, and employs a parallel arbitration mechanism to reconcile conflicting evidence without interference. On challenging benchmarks, CF-RAG substantially improves robustness against the Correlation Trap, achieving state-of-the-art performance while maintaining comparable efficiency to standard RAG models.
BibTeX
@inproceedings{
qin2026counterfactual,
title={Counterfactual Reasoning for Retrieval-Augmented Generation},
author={Huaiyu Qin and Chunyu Wei and Yueguo Chen and Yunhai Wang},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=9U51rOnGko}
}