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

arXiv: https://arxiv.org/abs/2409.02130 · HTML: https://mlsanity.com/paper/2409.02130/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency](https://mlsanity.com/paper/2407.09519/) (2024)
- [Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study](https://mlsanity.com/paper/2006.10092/) (2020)

## Built on by (most cited first, 1 of 1)

- [Causal-driven attribution (CDA): Estimating channel influence without user-level data](https://mlsanity.com/paper/2512.21211/) (2025)

## Abstract (arXiv)

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.