从预测重要性到因果性：哪个机器学习模型反映了现实？
From Predictive Importance to Causality: Which Machine Learning Model Reflects Reality?
AI总结：
本研究使用CatBoost和LightGBM分析Ames Housing Dataset，探索房价预测中特征重要性与因果关系，发现SHAP与因果特征间存在0.48的中等Spearman相关性，强调需结合预测与因果洞察。
摘要：
本研究使用CatBoost和LightGBM模型分析Ames Housing Dataset，以探索房价预测中的特征重要性与因果关系。我们考察了SHAP值与EconML预测之间的相关性，在价格预测中实现了高准确度。我们的分析揭示了基于SHAP的特征重要性与具有因果显著性的特征之间存在0.48的中等Spearman秩相关性，突显了在住房市场分析中将预测建模与因果理解相一致的复杂性。通过广泛的因果分析，包括异质性探索和政策树解释，我们提供了关于门廊等特定特征如何在各种场景下影响房价的见解。这项工作强调了在房地产估值中结合预测能力与因果洞察的集成方法的需求，为行业利益相关者提供了有价值的指导。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
