Title: DiCoFlex: Model-Agnostic Diverse Counterfactuals with Flexible Control
Venue: NeurIPS 2025
Accepted: yes
Keywords: Explainable AI

Counterfactual Explanations

Tabular Data

Normalizing Flows
Abstract: Counterfactual explanations play a pivotal role in explainable artificial intelligence (XAI) by offering intuitive, human-understandable alternatives that elucidate machine learning model decisions. Despite their significance, existing methods for generating counterfactuals often require constant access to the predictive model, involve computationally intensive optimization for each instance, and lack the flexibility to adapt to new user-defined constraints without retraining. In this paper, we propose DiCoFlex, a novel model-agnostic, conditional generative framework that produces multiple diverse counterfactuals in a single forward pass. Leveraging conditional normalizing flows trained solely on labeled data, DiCoFlex addresses key limitations by enabling real-time, user-driven customization of constraints such as sparsity and actionability at inference time. Extensive experiments on standard benchmark datasets show that DiCoFlex outperforms existing methods in terms of validity, diversity, proximity, and constraint adherence, making it a practical and scalable solution for counterfactual generation in sensitive decision-making domains.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new generative approach for providing diverse counterfactual explanations. A key idea of the proposed method, called DiCoFlex, is to train a normalizing flow model by taking validity, proximity, and plausibility into account. In addition, DiCoFlex can handle sparsity and actionability constraints using a customized proximity measure without requiring retraining. Through experiments, the authors demonstrated that DiCoFlex was significantly faster than existing baselines, while maintaining comparable or superior quality of generated counterfactuals.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The motivation of the proposed method is reasonable. I think it is an interesting idea to model the desiderata of counterfactual explanation, including not only plausibility but also actionability, by the normalizing flow. \n1. The authors demonstrated well that the proposed method was significantly faster than the baselines without compromising the quality of the generated counterfactuals.  \n\nWeaknesses:\n1. The presentation of the proposed method in Section 3 could be more clarified and improved. While Section 3 primarily focuses on modeling $q$ and learning it as $p_\\theta$, it remains unclear how to generate counterfactuals. If my understanding is correct, the proposed method generates a set of counterfactuals $x'$ by sampling from a trained normalizing flow model $p_\\theta(x' \\mid x, y', p, m)$. However, I could not find how the proposed method ensures the diversity of the generated counterfactuals. The score function defined in Equation (6) was confusing for me because it is not directly used to generate counterfactuals. \n1. Although the proposed method heavily relies on the normalizing flow, this paper lacks details on the training of the normalizing flows in the experiments. For example, the average running times of training the normalizing flow models are not reported in this paper. In addition, I could not find how the authors determined the hyperparameters of the proposed method, such as $K$, $\\alpha$, number of training steps $T$, and model complexity (e.g., hidden layer sizes). I am concerned that the choice of these parameters may affect the performance of the proposed method. \n1. (minor) I am concerned about the use of the $L_p$-norm with $p \\in (0, 1)$ (e.g., $p=0.01$), as it may lead to instability in gradient-based training. I wonder if the sparsity can be sufficiently encouraged by the general $L_1$-norm. \n1. (minor) Although the key technique of the proposed method is the normalizing flow, this paper appears to overlook existing methods that also leverage the normalizing flow [1, 2]. \n\n[1] Wielopolski et al.: Probabilistically Plausible Counterfactual Explanations with Normalizing Flows. arXiv, 2024.\n\n[2] Furman et al.: Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels. arXiv, 2024.\n\nQUESTIONS:\n1. In the experiments, the authors used five datasets with about 30000 samples. Can the proposed method perform similarly for smaller benchmark datasets, such as FICO and German?\n1. In Section 3.4, the authors state, \"While sparsity is often implicitly achieved for categorical variables due to the applied optimization scheme, enforcing sparsity on continuous features remains challenging.\" and \"Since categorical attributes are commonly encoded by one-hot vectors, the above scheme affects mostly continuous features.\" What do these sentences mean?\n\nLIMITATIONS:\nWhile the authors discussed the limitations of the proposed method in Appendix, there seems to be no discussion on its potential negative societal impacts. For example, I think the diverse counterfactual explanation poses a risk of model extraction [3, 4]. \n\n[3] Aïvodji et al.: Model extraction from counterfactual explanations. arXiv, 2020. \n\n[4] Wang et al.: DualCF: Efficient Model Extraction Attack from Counterfactual Explanations. FAccT, 2022.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-16"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces DiCoFlex, a conditional generative framework for model-agnostic counterfactual explanation. DiCoFlex is built upon conditional normalizing flows (specifically, Masked Autoregressive Flows) and trained using only labeled data, without requiring access to model gradients or internals. It enables generation of diverse, plausible, and constraint-aware counterfactuals in a single forward pass. The method is evaluated on five benchmark tabular datasets, where it outperforms or matches state-of-the-art baselines across key metrics including diversity, proximity, and plausibility.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nThe approach is model-agnostic and does not require gradient access or retraining when user constraints change.\n\nDiCoFlex supports dynamic inference-time control over constraints, which is both novel and practical.\n\nThe model is efficient: it generates multiple diverse counterfactuals per instance via a single forward pass.\n\nExperimental results are extensive and well-structured across multiple datasets and evaluation metrics.\n\nThe paper is highly reproducible, with code, datasets, evaluation procedures, and a detailed training algorithm provided.\n\n\n\nWeaknesses:\n\nWhile DiCoFlex claims inference-time controllability of sparsity via parameter p, the comparison between DiCoFlex and s-DiCoFlex is somewhat confusing , the paper treats them as different variants even though they use the same model with different p values.\n\nThe paper does not provide a sensitivity analysis or sweep over different values of p, which could strengthen the claim of flexible constraint handling.\n\nStatistical uncertainty (e.g., standard deviations, confidence intervals) is not reported in the evaluation.\n\nThe sparsity explanation in Section 4 contains unclear wording: it states “p = 0.01 encourages lower sparsity”, which contradicts the intended meaning (lower p encourages higher sparsity).\n\nQUESTIONS:\n1. DiCoFlex vs. s-DiCoFlex: Since p is part of the conditioning context and can be set dynamically at inference time, what exactly distinguishes s-DiCoFlex from DiCoFlex beyond using different p values? Are they trained with different p ranges, or is this merely a naming convention?\n\n2. Sensitivity analysis: Can the authors provide or discuss a broader sweep over p values to show how sparsity, plausibility, and proximity metrics vary as a function of p?\n\n3. Clarify the wording around the sparsity effect of low p: the paper says “lower sparsity” is encouraged when p = 0.01, but the intended behavior is to encourage higher sparsity.\n\n4. Context vector construction: Can the authors clarify how the conditioning vector (including p, mask, and target class) is structured and passed into the MAF layers?\n\nLIMITATIONS:\nAdditional reflection on robustness to labeling noise or fairness impacts of constraint settings would further strengthen this part.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

comments: SUMMARY:
This paper introduces DiCoFlex, a model-agnostic, conditional generative framework for generating diverse counterfactual explanations for tabular data. DiCoFlex leverages conditional normalizing flows to generate multiple plausible counterfactuals in a single forward pass. The results demonstrate that DiCoFlex achieves significant computational efficiency and flexibility for incorporating domain-specific constraints.

STRENGTHS AND WEAKNESSES:
## Strengths
1. The motivation is clear, and the proposed method is described in a detailed and structured way.
2. The experimental setup is thorough, with comprehensive evaluations across diverse datasets and meaningful comparisons to baseline methods.
3. The evaluation metrics for counterfactual quality, including diversity and plausibility provides extra perspectives.

## Weaknesses

1. There are gaps between the stated goals and the technical implementation. For example, the connection between validity, proximity, and plausibility and their calculation in Eq. (6) is not clearly articulated.
2. It is not clear how the score function (Eq. 4) and the actionable constraints Eq (9) are integrated into the training of the flow-based model.
3. The approach appears largely intuition-driven and lacks rigorous theoretical guarantees or proofs of properties such as diversity or constraint adherence.

QUESTIONS:
Please refer to the weakness

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03