Causal Evaluation of Planning Strategies in Large Language Models Through Interpretable Quality Prediction and Counterfactual Reinforcement Learning
Keywords:
causal evaluation, planning strategies, large language models, interpretable quality prediction, counterfactual reinforcement learning, SHAP, system architecture, governance, fairness, robustnessAbstract
Large language models have demonstrated remarkable reasoning capabilities, yet their planning strategies remain opaque and difficult to evaluate systematically. This paper proposes a causal evaluation framework that combines interpretable quality prediction with counterfactual reinforcement learning to assess and improve the planning processes of LLMs. We argue that traditional evaluation metrics based solely on final outcome accuracy are insufficient for understanding the structural causes of planning failures. Instead, we introduce a quality prediction model grounded in interpretable machine learning techniques, such as SHAP-based feature attribution, which provides a causal proxy for the intermediate reasoning steps. This proxy enables the detection of planning deficiencies at a granular level. Subsequently, we employ counterfactual reinforcement learning to generate alternative planning trajectories and optimize the decision-making policy under causal constraints. The framework addresses critical system-level concerns including architectural trade-offs between planning depth and computational cost, governance of model deployment, robustness to distributional shift, fairness across diverse input populations, and policy implications for accountable AI. We illustrate the approach through conceptual case studies involving multi-step reasoning tasks and tool-use scenarios. The findings suggest that integrating causal reasoning into LLM evaluation not only enhances planning quality but also fosters transparency and alignment with human values. This work provides a foundational methodology for building interpretable, robust, and ethically governed LLM planning systems.
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