From Predictive Importance to Causality: Which Machine Learning Model Reflects Reality?

TL;DR

This paper investigates the relationship between feature importance in machine learning models and causal effects in housing price prediction, emphasizing the complexity of aligning predictive accuracy with causal understanding.

Contribution

It introduces an integrated analysis combining SHAP values and causal inference methods to better interpret ML models in real estate valuation.

Findings

High accuracy in price forecasting using CatBoost and LightGBM.

Moderate correlation (0.48) between SHAP importance and causal features.

Insights into how features like porches influence prices across scenarios.

Abstract

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.