Title: Collective Counterfactual Explanations: Balancing Individual Goals and Collective Dynamics
Venue: NeurIPS 2025
Accepted: yes
Keywords: Counterfactual Explanation

Algorithmic Recourse

Population Dynamics

Unbalanced Optimal Transport
Abstract: Counterfactual explanations provide individuals with cost-optimal recommendations to achieve their desired outcomes. However, when a significant number of individuals seek similar state modifications, this individual-centric approach can inadvertently create competition and introduce unforeseen costs. Additionally, disregarding the underlying data distribution may lead to recommendations that individuals perceive as unusual or impractical.
To address these challenges, we propose a novel framework that extends standard counterfactual explanations by incorporating a population dynamics model. This framework penalizes deviations from equilibrium after individuals follow the recommendations, effectively mitigating externalities caused by correlated changes across the population. By balancing individual modification costs with their impact on others, our method ensures a more equitable and efficient outcome.
We show how this approach reframes the counterfactual explanation problem from an individual-centric task to a collective optimization problem. Augmenting our theoretical insights, we design and implement scalable algorithms for computing collective counterfactuals, showcasing their effectiveness and advantages over existing recourse methods, particularly in aligning with collective objectives.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a new framework for group-wise counterfactual explanation, named collective counterfactual explanation, that aims to maintain the distribution of perturbed instances and positive instances as unchanged as possible while minimizing the total modification costs. \nThe authors show that this task can be reduced to the optimal transport problem, which can be efficiently solved by existing algorithms such as the Sinkhorn algorithm. \nExperimental results demonstrated that the proposed method could achieve significantly lower competition costs than existing standard methods and balance the trade-off between competition cost and modification cost by tuning the hyperparameter.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. This paper is well-written and well-organized. \n1. The authors formulate a new, well-motivated problem of group-wise counterfactual explanation. I find it interesting to solve the formulated problem by leveraging techniques from optimal transport. \n1. The efficacy of the proposed method is well-supported by the experimental results. \n\nWeaknesses\n1. One of my concerns is that counterfactuals are limited to a predefined finite set of positive samples $\\mathcal{X}^+$, in contrast to the existing standard methods that optimize counterfactuals over an infinite set of perturbations. I think it is challenging for the proposed method to meet some of the desiderata of counterfactual explanation, such as sparsity. \n1. Another concern is the computational cost of the proposed method, as the experiments are limited to a setting where only two continuous features can be modified, and the average running time of the proposed method is not reported.\n\nQUESTIONS:\n1. In experiments, the authors state, \"We use only two continuous actionable features to determine the best recourse.\" What is the reason for this setting? Can the proposed method be applied to situations with more than two actionable features?\n1. How were the average running times of the proposed method compared to the other baseline methods?\n1. (typos) In line 28, \"desirable\" and \"undesirable\" may be reversed.\n\nLIMITATIONS:\nThe authors discuss the limitations of their work, but there is no discussion of its potential negative societal impacts.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-16"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nMost previous works on counterfactual explanations (CE) focus only on individual cases and overlook the externalities that occur when many people follow similar recommendations. This work proposes a new framework to address this limitation. Building on a population dynamics model, the authors introduce a penalty term into the standard CE objective to balance private and external costs and develop a scalable algorithm to solve it efficiently. In addition, they analyze how this approach improves over prior methods across different criteria for successful recourse and demonstrate its versatility by extending it to path-guided CE and ordered classifier families.\n\nSTRENGTHS AND WEAKNESSES:\nI recommend accepting this paper based on the following strengths:\n\n- On the topic of accounting for externalities, prior work [56] was the first to highlight the existence of this problem, but it only proposed some ad-hoc mitigation strategies. To the best of my knowledge, this work is the first to provide a complete and practical framework as a solution. For this reason, it is likely to serve as a solid benchmark for future research in this area. I also look forward to the authors releasing the source code.\n- The writing quality of this paper is good. The logic is clear and rigorous, and the authors have helpfully organized the necessary background material in Appendices A–C for better understanding. I briefly reviewed the proofs in Appendix D and did not notice any major issues. Additionally, the appendices detailedly provide the algorithms and experimental, ensuring the reproducibility of this work.\n- The experimental results strongly support the authors’ arguments. For example, Figures 1, 5b, and 6 demonstrate that the proposed method indeed produces actionable recommendations and that the individuals receiving recourse end up closer to a realistic distribution. Meanwhile, Figure 4 confirms that the approach balances private and external costs as expected.\n\nIn addition to these strengths, I would like to point out a few weaknesses that the authors could improve:\n\n- I would appreciate an explanation of why the regularization term corresponding to $\\lambda_1$ uses KL-divergence. Using different divergence measures for the two regularization terms could potentially introduce challenges, such as making the choice of $\\lambda_2$ more sensitive.\n- The assumption that “individuals are in equilibrium before CE” is an important premise in this paper. I suggest that the authors explicitly state this assumption in a formal way.\n\nNote that I didn't pay much attention to this field in the past, so I gave a relatively low confidence.\n\nQUESTIONS:\n1. Why use KL-divergence for the regularization term corresponding to $\\lambda_1$?\n2. What happens if the two regularization terms in Equation (6) use the same or different divergences? How does this affect choosing $\\lambda_1$ and $\\lambda_2$?\n3. Is using a discretized grid a good way to compute $\\chi^2$-divergence? It seems hard to control the grid resolution, and this might strongly affect the results.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work introduces a new formulation of counterfactual explanations generation which targets to evenly distribute the counterfactual points of negatively affected individuals within the data distribution of the favourable region. The motivation behind this approach is to mitigate the potential competition for limited resources after counterfactuals are implemented, especially if the counterfactuals mostly converge to similar spaces in the favourable region. The method is evaluated theoretically and experimentally. The evaluation involves 3 tabular datasets, 2 types of machine learning models (MLP and random forest), 6 baseline methods (2 of which target plausibility of counterfactuals). Results on the cost of competition are promising, but recourse cost (distance between counterfactuals and inputs) for the proposed method is not the best.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n- The paper is very complete and well-written with clear motivations, illustrations, assumptions, etc. \n- The mathematical formulations of the problem and the methodology are clear and easy to follow. Theoretical analyses are also adequate.\n- While the problem setting targeted in this paper seems very similar to the plausibility of counterfactuals (requiring the generated explanations to be located well within the data manifold), the characterisations regarding resources and equilibrium are novel.\n\nWeaknesses:\n- The main concern I have is the validity and significance of experimental results. Given the similarity between the problem setting and the plausibility of counterfactuals, it would make sense to compare the proposed method with existing methods which also target plausibility. There are 6 baselines involved in the experiment, but only 2 of them (CLUE and CCHVAE) consider the plausibility aspect. Those two methods both require, and are quite sensitive to the quality of, some external generative models. There exist many other methods that might perform better in terms of plausibility, therefore potentially better cost of competition. For example, FACE, DiCE (these two are also in the CARLA library), Guided Prototypes, MOC. All names are taken from Table 1, page 5 of Karimi et al.'s ACM Computing Survey (https://dl.acm.org/doi/pdf/10.1145/3527848). Personally, I've observed much better plausibility results from FACE and DiCE than CCHVAE. The presented good performance on cost of competition also comes with significant trade-offs with recourse cost, especially given that the baselines do not give the best recourse cost in the literature - those by mixed integer programming (e.g., Mohammadi et al. AIES 2021, https://dl.acm.org/doi/10.1145/3461702.3462514) usually perform better. I think addressing this point will make the paper a much stronger one.\n- In addition to the presented evaluation metrics, including quantitative measures for plausibility and computation time would be desirable.\n- Clarity on related works: following the above points, it'd be clearer if the characteristics, especially which desirable properties the baselines consider, are explicitly discussed.\n\nQUESTIONS:\n- Could you comment on how different your targeted problem setting is to the plausibility problem, i.e., requiring the generated explanations to be located well within the data manifold?\n- It seems to me intuitively that existing methods targeting plausibility and diversity, like DiCE or MOC, can naturally address the collective dynamics problem. Do you have any insights on that?\n- Minor point: there's a typo in 28 regarding desirable vs. undesirable; there's a dot above line 291 which takes some spaces.\n\nAlso see comments on weaknesses.\n\nLIMITATIONS:\nIt would be important to address specific differences between the targeted scenario and the plausibility problem.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Collective Counterfactual Explanations (CCE), which reframes algorithmic recourse as a collective optimization problem. By modeling population dynamics using mean-field game theory and optimal transport, CCE accounts for negative externalities when many individuals receive similar recommendations. It balances individual costs with collective impact by penalizing deviations from population equilibrium.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n- The paper addresses a problem of significant value within the field of counterfactual inference.\n- It provides solid theoretical results with proofs and demonstrates originality in its approach.\n\n**Weaknesses:**\n- My primary concern is the paper’s lack of completeness. In addition to not meeting the standard page count and lacking a dedicated conclusion section, the manuscript exhibits several formatting issues—such as a placeholder symbol (a centered dot above line 291), awkward paragraph breaks (e.g., lines 320, 348), and suboptimal paragraph structure in the final section—all of which make the paper feel unfinished.\n- The core \"equilibrium assumption\" is not discussed in sufficient detail regarding its limitations or its feasibility in real-world tasks.\n- There are multiple instances of incorrect citation formatting; the authors do not properly distinguish between \\citep and \\citet where appropriate (e.g., lines 230 and 232).\n- While the paper presents numerous numerical studies, the code is not provided, which prevents the verification or reproduction of the experimental results. The provided GitHub link directs to a repository for a prior work, which is an unnecessary and potentially misleading inclusion.\n\nQUESTIONS:\n- How robust is the CCE method to its core \"equilibrium assumption\"? If the initial population distribution is not in equilibrium before CE generation, how should the framework be adjusted?\n\n- See Weakness. Due to the paper’s lack of completeness, I believe it requires further refinement in both writing and formatting, as well as an additional round of review.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 3, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}