# Decomposing Global Feature Effects Based on Feature Interactions

arXiv: https://arxiv.org/abs/2306.00541 · HTML: https://mlsanity.com/paper/2306.00541/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2023.

## Built on by (most cited first, 17 of 17)

- [KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley Interactions](https://mlsanity.com/paper/2405.10852/) (2024)
- [A Guide to Feature Importance Methods for Scientific Inference](https://mlsanity.com/paper/2404.12862/) (2024)
- [Disentangling Interactions and Dependencies in Feature Attribution](https://mlsanity.com/paper/2410.23772/) (2024)
- [Regionally Additive Models: Explainable-by-design models minimizing feature interactions](https://mlsanity.com/paper/2309.12215/) (2023)
- [FINCH: Locally Visualizing Higher-Order Feature Interactions in Black Box Models](https://mlsanity.com/paper/2503.16445/) (2025)
- [Effector: A Python package for regional explanations](https://mlsanity.com/paper/2404.02629/) (2024)
- [GRANITE: A Generalized Regional Framework for Identifying Agreement in Feature-Based Explanations](https://mlsanity.com/paper/2601.22771/) (2026)
- [Statistical Inference and Learning for Shapley Additive Explanations (SHAP)](https://mlsanity.com/paper/2602.10532/) (2026)
- [Analyzing Error Sources in Global Feature Effect Estimation](https://mlsanity.com/paper/2603.15057/) (2026)
- [Succinct Interaction-Aware Explanations](https://mlsanity.com/paper/2402.05566/) (2024)
- [Implet: A Post-hoc Subsequence Explainer for Time Series Models](https://mlsanity.com/paper/2505.08748/) (2025)
- [KnowIt: Deep Time Series Modeling and Interpretation](https://mlsanity.com/paper/2507.06009/) (2025)
- [Provable Recovery of Locally Important Signed Features and Interactions from Random Forest](https://mlsanity.com/paper/2512.11081/) (2025)
- [Global Sensitivity Analysis for Engineering Design Based on Individual Conditional Expectations](https://mlsanity.com/paper/2512.11946/) (2025)
- [Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects](https://mlsanity.com/paper/2602.16503/) (2026)
- [SAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths](https://mlsanity.com/paper/2606.09404/) (2026)
- [We Need Explanation Cards to Connect Explanation Algorithms to the Real World](https://mlsanity.com/paper/2606.16786/) (2026)

## Abstract (arXiv)

Global feature effect methods, such as partial dependence plots, provide an intelligible visualization of the expected marginal feature effect. However, such global feature effect methods can be misleading, as they do not represent local feature effects of single observations well when feature interactions are present. We formally introduce generalized additive decomposition of global effects (GADGET), which is a new framework based on recursive partitioning to find interpretable regions in the feature space such that the interaction-related heterogeneity of local feature effects is minimized. We provide a mathematical foundation of the framework and show that it is applicable to the most popular methods to visualize marginal feature effects, namely partial dependence, accumulated local effects, and Shapley additive explanations (SHAP) dependence. Furthermore, we introduce and validate a new permutation-based interaction detection procedure that is applicable to any feature effect method that fits into our proposed framework. We empirically evaluate the theoretical characteristics of the proposed methods based on various feature effect methods in different experimental settings. Moreover, we apply our introduced methodology to three real-world examples to showcase their usefulness.