对比预测重要性与因果关系，发现房价模型中特征重要性关联有限。
From Predictive Importance to Causality: Which Machine Learning Model Reflects Reality?
- 用CatBoost和LightGBM分析房价数据，结合SHAP与因果推断方法。
- SHAP重要性与因果显著特征的秩相关系数为0.48，关联中等。
- 揭示了不同房型（如门廊）在不同场景下的价格影响机制。
本研究基于Ames住房数据集，采用CatBoost和LightGBM模型，探索房价预测中的特征重要性与因果关系。通过分析SHAP值与EconML预测结果的相关性，实现了高精度的价格预测。结果显示，基于SHAP的特征重要性与因果上显著的特征之间存在中等程度的Spearman秩相关性（0.48），表明预测建模与因果理解之间的复杂性。通过广泛的因果分析，包括异质性探索与政策树解释，揭示了诸如门廊等特定特征在不同情境下对房价的影响。该工作强调了在房地产估值中融合预测能力与因果洞察的重要性，为行业相关方提供了实用指导。
原文摘要 · Abstract (English)
This study analyzes the Ames Housing Dataset using CatBoost and LightGBM models to explore feature importance and causal relationships in housing price prediction. We examine the correlation between SHAP values and EconML predictions, achieving high accuracy in price forecasting. Our analysis reveals a moderate Spearman rank correlation of 0.48 between SHAP-based feature importance and causally significant features, highlighting the complexity of aligning predictive modeling with causal understanding in housing market analysis. Through extensive causal analysis, including heterogeneity exploration and policy tree interpretation, we provide insights into how specific features like porches impact housing prices across various scenarios. This work underscores the need for integrated approaches that combine predictive power with causal insights in real estate valuation, offering valuable guidance for stakeholders in the industry.
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