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

## TL;DR

This paper introduces the SIPA framework, unifying various model-agnostic interpretation methods into a single methodology to clarify their relationships and facilitate future research.

## Contribution

It presents a generalized SIPA framework that consolidates diverse interpretation techniques and extends it to feature importance measures, establishing a common terminology.

## Key findings

- Several feature effect methods are embedded into SIPA.
- Variance-based and performance-based importance measures are unified.
- The framework simplifies understanding of model-agnostic interpretation techniques.

## Abstract

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.