  Machine learning models are increasingly used in areas such as loan approvals
and hiring, yet they often function as black boxes, obscuring their
decision-making processes. Transparency is crucial, and individuals need
explanations to understand decisions, especially for the ones not desired by
the user. Ethical and legal considerations require informing individuals of
changes in input attribute values (features) that could lead to a desired
outcome for the user. Our work aims to generate counterfactual explanations by
considering causal dependencies between features. We present the CoGS
(Counterfactual Generation with s(CASP)) framework that utilizes the
goal-directed Answer Set Programming system s(CASP) to generate counterfactuals
from rule-based machine learning models, specifically the FOLD-SE algorithm.
CoGS computes realistic and causally consistent changes to attribute values
taking causal dependencies between them into account. It finds a path from an
undesired outcome to a desired one using counterfactuals. We present details of
the CoGS framework along with its evaluation.
