Title: Causal Explanation-Guided Learning for Organ Allocation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Causal Inference

Explanation Supervision

Organ Allocation
Abstract: A central challenge in organ transplantation is the extremely low acceptance rate of donor organ offers—typically in the single digits—leading to high discard rates and suboptimal use of available grafts. Current acceptance models embedded in allocation systems are non-causal, trained on observational data, and fail to generalize to policy-relevant counterfactuals. This limits their reliability for both policy evaluation and simulator-based optimization. In this work, we reframe organ offer acceptance as a counterfactual prediction problem and propose a method to learn from routinely recorded—but often overlooked—refusal explanations. These refusal reasons act as direction-only counterfactual signals: for example, a refusal reason such as "old donor age" implies acceptance might have occurred had the donor been younger. We formalize this setting and introduce ClexNet, a novel causal  model that learns policy-invariant representations via balanced training and an explanation-guided augmentation loss. On both synthetic and semi-synthetic data, ClexNet outperforms existing acceptance models in predictive performance, generalization, and calibration, offering a robust drop-in improvement for simulators and allocation policy evaluation. Beyond transplantation, our approach provides a general method for incorporating human direction-only explanations as a form of model supervision, improving performance in settings where only observational data is available.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method called CLEXNET for learning a model of organ offer acceptance. CLEXNET has two key components: 1) A scheme for learning balanced representations of organ clusters that removes observational bias via adversarial training, and 2) A data augmentation method and associated explanation loss for learning from refusal codes associated with organ offer rejections. On experiments with synthetic data, CLEXNET is shown to perform better (according to BCE, AUC, AUPRC, Brier score) than previous methods, including the same model without the explanation guided loss.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The work is well-motivated: The introduction lays out the key challenges that the work aims to address and how previous works have ailed to address these challenges.\n- The paper is generally well-organized (minus where the specific novel aspects of CLEXNET could be clarified -- see below).\n- The general idea of treating refusal codes as counterfactual explanations to augment data is simple yet clever.\n\nWeaknesses:\n- All experiments are on synthetic datasets, calling into question the robustness of the results. For example, it is not clear whether the rules used to map features in counterfactual explanations to directions would generalize to real-world datasets, where explanations and their relationships to datasets would be more complex.\n- The proposed method, CLEXNET, has several key steps, and it is unclear which are claimed to be novel (for example, just the method of learning from refusal codes, or also the balanced training step?). This could be clarified in the paper.\n\nMinor Notes:\nLine 102: \"The ranking... the sequence of potential transplant recipients *who* are considered\" --> missing \"who\"\n\nQUESTIONS:\n- Why do experiments only use synthetic datasets, rather than natural data, even for the observational dataset (line 237)?\n- Does the approach allow for interaction between the features cited in a contrastive counterfactual explanation? For example, what happens if two reasons are cited, and the reasons interact with each other to affect what the counterfactual outcome would have been (compared to if only a single reason were responsible for the organ rejection)?\n- Which parts of CLEXNET are novel compared to previous work on learning models of organ-offer acceptance?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

comments: SUMMARY:
The paper proposes a new machine learning method for organ allocation. The core idea is to utilize the categorical refusal reasons, which were not exploited in previous methods, to learn an embedding of counterfactual edits to the input features. Experiments on synthetic data show that the proposed model combined with this new source of information improves the acceptance rate over previous methods.

STRENGTHS AND WEAKNESSES:
**Strengths:**
- The method exploits a new source of information previously ignored by other methods: a categorical refusal reason (e.g. “donor too old”, “size mismatch”), which encodes rich information that can potentially improve the acceptance prediction.
- The refusal reason describes what feature causes the rejection, but does not indicate how much we should change that feature. The authors formulate an explanation loss to make the model obey the assumption that there exists some magnitude of change along the counterfactual direction to flip the prediction.
The presentation is clear, with easy-to-understand motivation and modeling details, for a reader not familiar with this topic to dive in.

**Weaknesses:**
- The paper uses synthetic data for all the experiments. I am not familiar enough with the norms of this field to know if this is widely accepted. The natural concern is the modeling method of the synthetic data may not fully reflect the complexity of the real-world problem, leading to biased conclusions.

QUESTIONS:
None

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

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses a critical limitation in organ transplantation acceptance modeling: existing machine learning models are trained on observational data and fail to generalize to counterfactual policy scenarios. To address this, the authors propose CLEXNET, a causal and explanation-guided model that integrates direction-only refusal reasons (e.g., \"donor too old\") as contrastive signals to guide learning. The model combines adversarial balancing to correct for policy-induced bias and a guarded explanation-guided augmentation loss to enforce alignment with refusal reasons. Empirical evaluations on synthetic data show that CLEXNET outperforms baselines in predictive accuracy, generalization, and robustness under increasing confounding. The framework is also positioned as a general recipe for counterfactual learning with direction-only supervision.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1) The paper tackles an important and underexplored issue in organ allocation, where offer rejections have cascading systemic effects.\n2) It formalizes a new setting where only directional feedback is available (refusal codes), distinct from existing gradient- or distance-based explanation-guided learning methods.\n3) The proposed CLEXNET architecture cleverly integrates domain adversarial training (to mitigate confounding) with a contrastive loss grounded in direction-only counterfactual edits.\nWeaknesses:\n1) All results are from synthetic datasets, which, while carefully constructed, may not fully capture the complexity and noise in real clinical data.\n2) In practice, refusal codes may be noisy, inconsistently recorded, or influenced by institutional policies, which is not discussed.\n3) While the mechanism is intuitive, human validation (e.g., by clinicians) on whether these augmentations reflect realistic edits is missing.\n\nQUESTIONS:\n1.  Do you plan to validate CLEXNET on real-world transplantation datasets, e.g., UNOS or Eurotransplant? How transferable are the findings beyond synthetic data?\n2. How does CLEXNET handle potentially incorrect or missing refusal reasons? Would soft or probabilistic embeddings of reasons be more robust?\n3. Have clinicians reviewed the counterfactual directions sampled from delta? Are some refusal categories more reliably directional than others?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose CLEXNET, a framework for modeling transplant offer acceptance. They note that prior approaches rely heavily on observational data and struggle to generalize to policy-relevant counterfactual scenarios. To address this, they reframe the acceptance problem as a counterfactual prediction task and leverage categorical refusal codes—recorded with each rejected organ offer—as direction-only signals for potential acceptance. Through synthetic experiments, CLEXNET demonstrates improved generalization and robustness compared to prior state-of-the-art models.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n* The paper is well-structured, with a clear and precise formulation of the problem and a well-explained method.\n* The motivation is compelling, addressing a real and important limitation in existing organ allocation models.\n* The experimental results on synthetic data support the authors’ claims regarding generalization and robustness.\n\n### Weaknesses\n* The evaluation is limited to synthetic datasets; no real-world data or case studies are used to validate the model's practical utility.\n* The method relies on strong assumptions about the informativeness and consistency of refusal reasons, which may not hold in real clinical settings.\n\nQUESTIONS:\n* Real-world validation: Can the authors provide any empirical or qualitative evidence that the model behaves sensibly when applied to real transplant data? \n* Strength of Assumption 1: Assumption 1—that \"for every refusal, there exists a feasible edit along the refusal reason direction that would result in acceptance\"—seems quite strong. Can the authors justify this assumption? How realistic is it in actual clinical workflows?\n* Comparison to Rule-Based Systems: How does CLEXNET perform relative to existing rule-based systems or policies?\n\nLIMITATIONS:\nThe lack of real world data is not discussed.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-11"}