Research on the Application of Causal Reasoning Method in Content Compliance Experimental Evaluation

This study improves the reliability of content conformity evaluation through causal reasoning.To address the limitations of large language models in reasoning reliability, a generator-validator collaborative framework is constructed.It integrates causal interpretation and reasoning tasks through a "generate-validate-correct" closed-loop mechanism, tackling challenges of high-dimensional sparsity and unstructured data.The traditional chain-of-thought method lacks process supervision, leading to logical discontinuity and error accumulation.Research has achieved breakthroughs through threestage innovation: in the mathematical reasoning scenario, the generator constructs a structured reasoning path and maps it to mathematical expressions, the validator provides fine-grained feedback, and the model accuracy and reliability have been significantly improved through six datasets validation and ablation experiments; The dimensions of causal reasoning are unified for causal inference and explanation generation.The generator extracts text causal relationships, and the validator generates natural language explanations to optimize the inference chain.Multi dimensional evaluation indicators and new quality scores are used to verify effectiveness, and the ablation experiment clarifies the impact of feedback forms; Under the requirement of autonomous causal reasoning, a causal chain prompt framework is designed to transform the five step process of variable identification, relationship extraction, adjacency matrix initialization, independence evaluation, and hypothesis generation into executable prompt engineering.The intermediate process explicit output is achieved by integrating do calculus and d-separation principle, and breaking through data dependence by combining few sample learning.Experiments confirm the framework enhances reasoning accuracy, reduces long-term dependency errors, and improves interpretability.Future work will explore multimodal fusion and selfvalidation to advance reliable, interpretable cross-modal causal inference.