GRETEL: A unified framework for Graph Counterfactual Explanation Evaluation

TL;DR

GRETEL is a comprehensive, extensible framework designed to evaluate Graph Counterfactual Explanations across various datasets, models, and explanation methods, promoting reproducibility and standardization in the field.

Contribution

It introduces GRETEL, a unified, extensible evaluation framework for Graph Counterfactual Explanations, enabling consistent comparison and fostering open science practices.

Findings

GRETEL successfully integrates multiple datasets and explanation techniques.

The framework facilitates reproducible and standardized evaluation of GCE methods.

Experiments demonstrate GRETEL's effectiveness in benchmarking diverse GCE approaches.

Abstract

Machine Learning (ML) systems are a building part of the modern tools which impact our daily life in several application domains. Due to their black-box nature, those systems are hardly adopted in application domains (e.g. health, finance) where understanding the decision process is of paramount importance. Explanation methods were developed to explain how the ML model has taken a specific decision for a given case/instance. Graph Counterfactual Explanations (GCE) is one of the explanation techniques adopted in the Graph Learning domain. The existing works of Graph Counterfactual Explanations diverge mostly in the problem definition, application domain, test data, and evaluation metrics, and most existing works do not compare exhaustively against other counterfactual explanation techniques present in the literature. We present GRETEL, a unified framework to develop and test GCE methods in several settings. GRETEL is a highly extensible evaluation framework which promotes the Open Science and the evaluations reproducibility by providing a set of well-defined mechanisms to integrate and manage easily: both real and synthetic datasets, ML models, state-of-the-art explanation techniques, and evaluation measures. To present GRETEL, we show the experiments conducted to integrate and test several synthetic and real datasets with several existing explanation techniques and base ML models.