Causal Inference-Enhanced RAG for Reliable Reasoning Under Conflicting and Incomplete Evidence
Keywords:
retrieval-augmented generation, causal inference, conflicting evidence, incomplete evidence, uncertainty calibration, AI governance, socio-technical systems, reliable reasoningAbstract
Retrieval-augmented generation has become a central architecture for grounding large language models in external knowledge, yet its reliability remains fragile when retrieved evidence is contradictory, incomplete, or drawn from heterogeneous sources. Conventional retrieval and generation pipelines optimize semantic relevance and fluency rather than causal validity, making them vulnerable to spurious correlations, source selection bias, and unstable reasoning under distribution shift. This paper examines the system-level design of causal inference-enhanced retrieval-augmented generation as a response to these limitations. It argues that causal inference offers structural resources for distinguishing correlation from intervention, for modeling missingness and confounding, and for representing competing explanations rather than collapsing them into a single context window. The discussion emphasizes architecture, governance, deployment, sustainability, robustness, fairness, and policy implications. A causal-enhanced RAG system is conceptualized as a socio-technical infrastructure in which retrieval, evidence weighting, counterfactual querying, uncertainty calibration, and auditability are integrated across the lifecycle. The paper considers how such systems can manage conflicting evidence through explicit causal graphs, sensitivity analysis, and provenance-aware aggregation, and how incomplete evidence can be addressed through counterfactual retrieval, missingness indicators, and iterative evidence acquisition. It also explores the trade-offs between causal rigor and operational latency, the risks of fairness gerrymandering, and the need for institutional accountability. The conclusion is that causal inference should not be treated as a decorative layer but as an architectural discipline that shapes how evidence is selected, contested, and governed in high-stakes reasoning systems.
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