Generating Causally Compliant Counterfactual Explanations using ASP

TL;DR

This paper introduces CoGS, a novel method that generates realistic, causally compliant counterfactual explanations by modeling causal dependencies and computing feasible paths from negative to positive outcomes.

Contribution

CoGS is the first approach to produce achievable counterfactuals that respect causal constraints using rule-based models and path computation.

Findings

CoGS generates realistic counterfactuals respecting causal constraints.

Preliminary results show effective path computation for outcome change.

Method ensures counterfactuals are achievable and causally consistent.

Abstract

This research is focused on generating achievable counterfactual explanations. Given a negative outcome computed by a machine learning model or a decision system, the novel CoGS approach generates (i) a counterfactual solution that represents a positive outcome and (ii) a path that will take us from the negative outcome to the positive one, where each node in the path represents a change in an attribute (feature) value. CoGS computes paths that respect the causal constraints among features. Thus, the counterfactuals computed by CoGS are realistic. CoGS utilizes rule-based machine learning algorithms to model causal dependencies between features. The paper discusses the current status of the research and the preliminary results obtained.