🤖 AI Summary
This paper addresses the problem of generating *implementable counterfactual explanations*: given a negative prediction from a machine learning model, the goal is to produce a multi-step sequence of feature interventions that leads to a positive outcome while strictly respecting inter-feature causal constraints. Methodologically, the authors propose CoGS—a novel framework that integrates causal graph modeling with Answer Set Programming (ASP). It encodes causal dependencies as logical rules and leverages ASP solvers to guarantee that each intervention step adheres to the underlying causal structure; a dedicated path-search algorithm further ensures computational efficiency and solution feasibility. Empirically, CoGS achieves superior performance across multiple benchmark datasets: its generated counterfactual paths are simultaneously causally valid, semantically plausible, and operationally feasible—outperforming existing single-step and causally agnostic approaches.
📝 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.