aiqbench analysis
This benchmark evaluates LLMs' ability to handle context-dependent causal reasoning in economics, revealing significant performance drops when models fail to adjust for changing institutional or market conditions. The findings highlight critical limitations in current AI systems regarding source-sign over-transfer and the difficulty of predicting nonsignificant effects, offering a rigorous testbed for improving decision-support capabilities.
EconCausal: A Context-Aware Economic Reasoning Benchmark for Large Language Models
arXiv:2510.07231v5 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 causal triplets extracted from 2,595 high-quality empirical studies in top-tier economics and finance journals, constructed through a rigorous four-stage pipeline with multi-run consensus, context refinement, and multi-critic filtering. Across 19 models, we observe source-sign over-transfer in cross-context prediction. While top models reach ~84% accuracy in single-context prediction, mean accuracy is 74.75% overall but drops to 44.27% when source and target signs disagree. On these cases, accuracy is 11.54 percentage points (pp) lower with the signed source example than with the target input alone. Mean accuracy is below 60% under corrupted source signs. Models also struggle to predict study-reported nonsignificant effects (19.16% accuracy). The dataset and benchmark are publicly available at https://anonymous.4open.science/r/econcausal-benchmark-6F12.
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 causal triplets extracted from 2,595 high-quality empirical studies in top-tier economics and finance journals, constructed through a rigorous four-stage pipeline with multi-run consensus, context refinement, and multi-critic filtering. Across 19 models, we observe source-sign over-transfer in cross-context prediction. While top models reach ~84% accuracy in single-context prediction, mean accuracy is 74.75% overall but drops to 44.27% when source and target signs disagree. On these cases, accuracy is 11.54 percentage points (pp) lower with the signed source example than with the target input alone. Mean accuracy is below 60% under corrupted source signs. Models also struggle to predict study-reported nonsignificant effects (19.16% accuracy). The dataset and benchmark are publicly available at this https URL.
29 pages. Revised version with updated evaluation results, robustness analyses, expert validation, and references
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2510.07231 [cs.CL]
(or arXiv:2510.07231v5 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2510.07231 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 297 words
