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• InterveneBench is a benchmark comprising 744 peer-reviewed studies across diverse policy domains for assessing LLMs' intervention reasoning in realistic social settings.method 95%
• State-of-the-art LLMs struggle with intervention reasoning and causal study design in real social systems as evaluated by InterveneBench.result 90%
• The proposed multi-agent framework STRIDES achieves significant performance improvements over state-of-the-art reasoning models.result 90%
• Current benchmarks fail to evaluate LLMs' capability for end-to-end, intervention-centered research-design reasoning grounded in real-world policy interventions.limitation 85%
• There is a growing market demand for AI-driven decision support in public and private sectors for causal reasoning about interventions.market 80%
• Government agencies, non-profits, and large corporations would pay for a product based on this research to evaluate intervention effectiveness.market 75%
• LLMs may produce biased or inaccurate causal inferences if trained on flawed or limited data.limitation 85%
• The benchmark requires models to reason about policy interventions and identification assumptions without access to predefined causal graphs or structural equations.technical 90%

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