Density-Guided Robust Counterfactual Explanations on Tabular Data under Model Multiplicity

• URL: http://arxiv.org/abs/2605.30901v1
• Date: Fri, 29 May 2026 06:36:33 GMT
• Title: Density-Guided Robust Counterfactual Explanations on Tabular Data under Model Multiplicity
• Abstract summary: Counterfactual explanations (CEs) are essential for actionable recourse, yet their reliability is often compromised in low-density regions.<n>We propose textitDensityFlow, a generative framework that constructs robust CEs by adhering to the high-confidence data manifold.
• Score: 20.899371081590342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Counterfactual explanations (CEs) are essential for actionable recourse, yet their reliability is often compromised in low-density regions, where classifiers exhibit high variance. Unlike existing methods that rely on expensive ensemble intersections to define stability, we propose \textit{DensityFlow}, a generative framework that constructs robust CEs by adhering to the high-confidence data manifold. Specifically, we model the counterfactual generation as continuous-time dynamics parameterized by Neural ODE, guided by a differentiable density score to actively avoid uncertain, low-density areas. This density score is learned via Noise Contrastive Estimation, effectively leveraging a $(K{+}1)$-way discriminator to estimate density ratios. For black-box settings, we introduce a local proxy distillation mechanism that aligns a lightweight surrogate with the target model strictly within the trajectory of CE generation, enabling efficient gradient-based optimization with minimal queries. Experiments demonstrate that \textit{DensityFlow} achieves superior validity under model multiplicity while significantly reducing query costs compared to ensemble-based baselines. Our implementation is available at https://github.com/G-AILab/DensityFlow.

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