# Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations

arXiv: https://arxiv.org/abs/1904.03959 · HTML: https://mlsanity.com/paper/1904.03959/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2019.

## Built on by (most cited first, 9 of 9)

- [Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process](https://mlsanity.com/paper/2109.01433/) (2021)
- [Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena](https://mlsanity.com/paper/2206.05487/) (2022)
- [Grouped Feature Importance and Combined Features Effect Plot](https://mlsanity.com/paper/2104.11688/) (2021)
- [Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models](https://mlsanity.com/paper/2209.11539/) (2022)
- [Efficient and Accurate Explanation Estimation with Distribution Compression](https://mlsanity.com/paper/2406.18334/) (2024)
- [Interpretability from a new lens: Integrating Stratification and Domain knowledge for Biomedical Applications](https://mlsanity.com/paper/2303.09322/) (2023)
- [fmeffects: An R Package for Forward Marginal Effects](https://mlsanity.com/paper/2310.02008/) (2023)
- [NIMO: a Nonlinear Interpretable MOdel](https://mlsanity.com/paper/2506.05059/) (2025)
- [Explainable AI Isn't Enough! Rethinking Algorithmic Contestability](https://mlsanity.com/paper/2605.16041/) (2026)

## Abstract (arXiv)

Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model. Due to different notations and terminology, it is difficult to see how they are related. A unified view on these methods has been missing. We present the generalized SIPA (sampling, intervention, prediction, aggregation) framework of work stages for model-agnostic interpretations and demonstrate how several prominent methods for feature effects can be embedded into the proposed framework. Furthermore, we extend the framework to feature importance computations by pointing out how variance-based and performance-based importance measures are based on the same work stages. The SIPA framework reduces the diverse set of model-agnostic techniques to a single methodology and establishes a common terminology to discuss them in future work.