CoGS: Model Agnostic Causality Constrained Counterfactual Explanations using goal-directed ASP

TL;DR

CoGS is a model-agnostic framework that generates causally consistent counterfactual explanations for machine learning models using goal-directed Answer Set Programming, enhancing interpretability and actionability.

Contribution

It introduces a novel approach combining ASP and rule-based ML to produce realistic counterfactuals that respect causal dependencies, improving explanation quality.

Findings

Successfully generates realistic counterfactual explanations.

Ensures causal consistency in feature modifications.

Applicable to various classification models.

Abstract

Machine learning models are increasingly used in critical areas such as loan approvals and hiring, yet they often function as black boxes, obscuring their decision-making processes. Transparency is crucial, as individuals need explanations to understand decisions, primarily if the decisions result in an undesired outcome. Our work introduces CoGS (Counterfactual Generation with s(CASP)), a model-agnostic framework capable of generating counterfactual explanations for classification models. CoGS leverages the goal-directed Answer Set Programming system s(CASP) to compute realistic and causally consistent modifications to feature values, accounting for causal dependencies between them. By using rule-based machine learning algorithms (RBML), notably the FOLD-SE algorithm, CoGS extracts the underlying logic of a statistical model to generate counterfactual solutions. By tracing a step-by-step path from an undesired outcome to a desired one, CoGS offers interpretable and actionable explanations of the changes required to achieve the desired outcome. We present details of the CoGS framework along with its evaluation.