arXiv:2510.07231v4 Announce Type: replace Abstract: Socio-economic causal effects depend heavily on their institutional and environmental contexts. The same intervention can produce different, even opposite, effects across regulatory regimes, market conditions, time periods, or populations. This poses a challenge for large language models (LLMs) in decision-support roles: can they infer the direction of a causal effect under a specified context, and revise that judgment when the context changes? To address this, we introduce EconCausal, a large-scale benchmark of 10,490 context-annotated causa
The rapid advancement and deployment of large language models into decision-support roles necessitates robust benchmarks to evaluate their contextual understanding and reasoning capabilities.
EconCausal addresses a critical limitation of LLMs in socio-economic domains, highlighting that their utility depends on the ability to understand nuanced causal effects within specific contexts, directly impacting their reliability for strategic decision-making.
The introduction of a specialized benchmark like EconCausal provides a standardized method to assess and improve LLMs' contextual economic reasoning, potentially leading to more sophisticated and reliable AI applications in finance, policy, and market analysis.
- · AI developers
- · Economists
- · Policymakers
- · Financial institutions
- · LLMs with poor contextual reasoning
- · Companies relying on superficial AI analyses
LLMs will be trained and fine-tuned specifically to improve their contextual economic reasoning based on benchmarks like EconCausal.
Improved economic decision-making by LLMs could lead to more stable markets and more effective policy recommendations.
The development of highly context-aware LLMs might accelerate the automation of complex economic analysis tasks, potentially altering the demand for human economic analysts.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL