用大模型分析假设情境下的贷款收益，效果接近传统机器学习。
Counterfactual Analysis via Large Language Models
- 用提示工程优化GPT-3.5预测能力
- 提示工程使R²从1.97%提升至2.84%
- 适合金融决策与可解释性要求高的场景
反事实分析旨在预测假设情景下的潜在结果，为决策提供重要参考。本文研究GPT-3.5在在线贷款场景中进行反事实分析的应用，重点关注不同利率方案下的反事实投资回报率（ROI）。首先评估GPT的预测性能，并与先进机器学习算法对比。结果表明，通过提示工程可显著提升GPT预测效果，其决定系数R²从1.97%提升至2.84%，接近梯度提升回归模型的3.48%表现。随后，利用GPT生成一系列替代利率下的反事实ROI，模型展现出逻辑连贯性和因果推理能力。研究证实大语言模型在在线贷款反事实分析中具有应用潜力，暗示其在各类预测与决策场景中的广泛适用性。
原文摘要 · Abstract (English)
Counterfactual analysis aims to predict potential outcomes under hypothetical scenarios, offering valuable insights for decision-making. This paper investigates the application of large language models (LLMs), specifically the GPT-3.5 model, for counterfactual analysis. We focus on the online lending context, where the counterfactual return on investment (ROI) is crucial for evaluating different interest rate schemes. We begin by assessing the predictive performance of GPT and comparing it with advanced machine learning algorithms. The results show that prompt engineering can significantly enhance GPT's predictions, with the R-squared increasing from 1.97% to 2.84%, closely approaching the 3.48% achieved by gradient-boosted regression. Subsequently, we utilize GPT to generate counterfactual ROIs under a set of alternative interest rates. GPT exhibits logical coherence and causal reasoning in its responses. The findings underscore the potential of LLMs as effective tools for counterfactual analysis in online lending, suggesting broader applications for LLMs in various predictive and decision-making contexts.
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