Causal Explanation-Guided Learning for Organ Allocation
Authors: Alessandro Marchese
Jeroen Berrevoets
Sam Verboven
Venue: NeurIPS 2025 poster ()
Keywords: Causal Inference
Explanation Supervision
Organ Allocation
Primary area: deep_learning
TL;DR: We develop a causal acceptance model for organ offers that learns from directional refusal reasons. Our method, ClexNet, leverages these explanations to guide learning and improve generalization beyond observed allocation policies.

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.

Causal Explanation-Guided Learning for Organ Allocation
Alessandro Marchese Vrije Universiteit Brussel
Jeroen Berrevoets King's College London
Sam Verboven Vrije Universiteit Brussel
Abstract
A central challenge in organ transplantation is the extremely low acceptance rate of donor organ ttyiial-yy the single digits-leading 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 generalize to policy-relevant counterfactuals. This limits their reliability for both policy evaluation and simulator based optimization. this work, we reframe organ offer acceptance
counterfactual prediction problem and propose a method to learn from routinely recorded
often werlooked-refiin explanations. These refusal reasons act as direction-only counterfactual signals: for example, refusal reason such as "old donor age" implies acceptance might have occurred had the donor been younger. We formalize this setting and introduce CLEEXNT, novel causal model that learns policy-invarian representations via balanced training and explanation-guided augmentation loss. On both synthetic and semi-synthetic data, CLEXNET outperforms existing acceptance models in predictive performance, generalization, and calibration, offering robust drop-in improvement for sim- ulators and allocation policy evaluation. Beyond transplantation, our approach provides general method for incorporating human direction-oni only explanations as form of model supervision, improving performance in settings where only observational data is available.
Introduction
Organ transplantation is often the definitive treatment for end-stage organ failure. Yet demand for donor organs persistently outweighs supply [36). As such, many patients deteriorate or die while waiting suitable donor 6T M] Optimizing allocation systems to match donated organs with compatible recipients is therefore a critical task.
Organ offer refusals cause widespread inefficiencies in transplant systems. The acceptance rate of organ offers in the U.S. is extremely low: only 1% of kidney, 3% of liver, and 5% of lung offers are accepted (T. Each refusal not only delays transplantation for the recipient but triggers cascading effect across the system. Organs accumulate cold ischemic time as they are offered down the ranked waitlist, increasing the risk of graft failure and reducing transplant success Moreover, extended offer chains burden allocation logistics and significantly increase the probability
35 38 20)
of eventual organ discard 42 177
Existing acceptance models are oversimplified and unfit for counterfactual estimation. Current ML-based allocation studies sidestep the complexity refusals, assuming every offer accepted [ 6. 7 65 (66). This simplifying assumption breaks down in practice, leading to unrealistic simulations
and misinformed policy guidance. Even real-world simulators used by transplant organizations that
Coorrespndingg author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Ranked by Tobs
0
Old donor
01 61
X
refuse
CU202
Hight cold ischemic
ofter O
2
Doba
Accepted
Decision boundary
offer
X3
X
X2
X3
X
Figure 1: Illustrative overview of organ offer acceptance. Left: An incoming liver offer O₃ is broadcasted down the Observed-policy Tobs ranking. Each candidate X; either accepts (green tick) categorical refusal reason (call-outs). Right:
or refuses (red cross) and Geometric view of patient space X' and organ offer space O, The blue shaded region marks the domain covered by the observational dataset Dabs: the dashed purple curve encloses the full feasible domain F. Dashed arrows show atomic refusal-reason directions, solid arrows contrastive counterfactual edits a,,; that would cross the decision boundary and convert a refusal into an acceptance. For example, based on the factual samples in Doba and direction 8,, a model should learn that the decision boundary lies between the intersected boundaries (the yellow points) of Dobs and F.
incorporate acceptance models 49
44
45
fall short for two key reasons. First, they are typically which, while interpretable, lacks the capacity to
based on logistic regression (54 model the complex, high-dimenssonal interactions involved in clinical decision making. Modern
56
55
18 19,
machine learning alternatives offer significantly greater expressiveness [64] TO 67 40]. Second, these models are trained observational data generated under the current policy, inheriting spurious of shortcut learning (23 -that lead to confounding bias (5), and failure
to generalize to counterfactual policy-relevant scenarios. Even after adjusting for confounding bias, reliable generalization to unseen counterfactual policies remains difficult without additional sources
of information.
Refusal reasons are untapped direction-only contrastive explanations. A key yet unused source of supervision to improve acceptance models lies the refusal reasons recorded with each declined of- fer [12 29 These categorical reasons for example, "old donor age" or "high cold ischemic time" " act as directional signals, suggesting how a donor or recipient attribute could modified to change the acceptance outcome. Crucially, these signals are contrastive but not quantitative: they indicate which features to adjust, but not by how much, nor whether such adjustments are minimal, or whether the response surface is local monotonic.
Current Applanation-uuides methods cannot handle direction-only explanations. Existing explanation-guider methods require richer forms of supervision such as precise counterfactual distances 24 21. local input-gradient constraints [47 [59 global monotonicity rules [25] 37). As such, these methods are ill-suited for this setting in which only a sparse, directional signal is available. Bridging this methodological gap is crucial for building unbiased acceptance models and fully utilizing the explanatory power of this routinely collected data.
Our contributions.
We propose a new learning framework that formalizes this novel supervision regime and introduce CLEXNET. Our contributions are threefold:

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We formalize new learning setting in which the only extra supervision available is a set of categorical refusal reasons that point out a direction-bul but never the magnitude- which donor or recipient attributes would need to change for organ offer to be accepted.
We construct guarded explanation- -aiiidddgggdddd augmentation scheme that converts each cate- gorical refusal reason into a set of feasible counterfactual edits, enabling any classifier to learn from direction-only feedback without needing distance or monotonicity information.
We introduce CLEXNET, causally calibrated acceptance model that learns policy-invarian representations using adversarial balancing and an explanation-guidee augmentation loss. This model simultaneously corrects for observational bias and respects directional refusal constraints.
Through comprehensive synthetic and semi-synthetic experiments, we demonstrate that CLEXNET outperforms existing acceptance models in generalization, calibration, and predietive accu- secy-offering a practical and robust improvement for policy simulators. More broadly, our approach opens new direction in counterfactual machine learning by operationalizing contrastive human feedback in high-stakes, observational settings like organ transplantation.
Problem formulation
During the organ offer process, the organ is repeatedly offered electronically to potential transplant recipients in ranking until potential recipient accepts the offer as shown in Figure I the offer made to each patient is modeled, and the mathematical notation is introduced.
In this section,
Making offer. Consider X C Rd as the space of all possible patients and O C [Rde as the space of all possible organ offers. Let X E X' and O E O be the feature vectors of a patient and an organ offer respectively.
O
After an offer has been made, an answer Y E (0,1}, where represents a refusal and represents accep- tance, is received from the patient. When patient de- clines an offer, they must provide reason. Consider R. = (2, r') as the set of all possible refusal rea- son categories. Each r E R represents a discrete category for the reason why an offer might be refused. For example, these categories might correspond to issues related to the donor age the cold ischemic time of the organ. Finally, let R E R U {0} denote a variable representing such rea- son, allowing for the absence of reason, represented by R = 0. in the case of acceptance.
Y
R
Figure 2: Graphical structure of or- gan offer and patient response. An organ offer O is extended, following the existing policy Toha, to candidate pa- tient characterized by X. The patient re- sponds with a binary decision Y, which is determined by both the patient's 's fea- tures and the offer's features. In cases of refusal, the patient supplies refusal rea- son R, which can be expressed in terms of X and O through predefined map- M..
We assume we have an observational dataset containing N instances of patient-offer pairs and their corresponding answer and reason, resulting in dataset of observations of quadruplets = {(,,, O, Y,, R2) : = I.... K) J. where all patients and organs distributions p(X) and p( (O)) Finally, consider Q E P())
sampled from some underlying
as set that represents the wait list.
The dataset D is observed under policy Tobs : Q x O - Gg where 6 denotes the generated ranking of patients in the wait list. The ranking Gg directly dictates the sequence of potential transplant recipients are considered. As result, the generation the observed dataset 'D is inherently influenced by Robs- A graphical representation of the underlying structure of 'D is shown in Figure 2
For both the answers, Y; and the reasons Rd we assume that they are generated following the Rubin- Neyman potential outcomes framework (48). For each patient there are two sets of potential outcomes (Y (o) O} and (R(o) o E O). and that the observed outcomes Y, and C, are consistent with the potential outcomes Y(O:) and R(O,) for the observed offer.
2-))) is the power set of given set.
( the symmetric group of given set.
3

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Interpreting direction-only reasons. Consider A (2,00) and let M R - A denote function that maps refusal categories onto corresponding signed vector embeddings the constrained patient-offer space A. For any refusal category E R, the embedded reason is d E A, given by M(r)
The embedded reason represents the direction of a contrastive counterfactual explanation [51] :1: in what direction the features of X and O have change such that the offer would get accepted. As the complete counterfactual explanations are not disclosed, for each continuous feature we only encode one of three possible options for the direction: decrease (-1), remain unchanged (0) and increase (1).
Assumption 1 (Counterfactual existence). Consider 6x and d as vectors composed by the elements of 5 that correspond to the features of X and O respectively such that ő (ox. 6o). For any observed refusal with a refusal reason R & @ with corresponding embedding 6, we assume that:
(1) where ax and ao are vectors that represent the needed magnitudes of change in the features of X and O respectively and (ax, ao). In words, this assumption states that positive counterfactual can be found if a factual negative sample is edited along the direction
Eax R ao E R +xx+aaxxxx O aggo) =
Assumption 2 (Feasible magnitude). Let F C X U O be realistic and feasible region of patient-organ pairs. Consider A as the space of all edit magnitudes such that:
Ya E A: (X + axxx, O + aodo) F
With this assumption, IF limits the possible edit magnitudes such that the counterfactuals would remain in a specific, predefined region. For example, would not make sense if O + aooo results in an organ from a donor with a negative age feature. Graphical representations of reasons transformed into counterfactual edits are shown Figure]
Causal assumptions. To identify the effects of organ offers on patient responses we impose three standard causal assumptions. First, positivity (or overlap) requires that for every patient-offe feature pair (X, O) such that X has nonzero support in the wait-list distribution, the observed policy Tobs assigns a strictly positive probability of that offer being made, i.e. Pr(O) X) > O. Second, unconfoundedners (or ignorability) assumes that, conditional on the patient and offer covariates, the
pair of potential outcomes (o),, R(o) ooc O} is independent of the ranking and assignment mechanism, formally:
(Y REO II Tobs X.
Finally, we assume the stable unit treatment value assumption (SUTVA), which consists of two parts: (i) consistency, meaning that the observed outcome and refusal reason coincide with the corresponding potential outcomes under the realized offer, Y Y(O) and R R(O), and (ii) no interference, meaning that one patient's 's response and reason are unaffected by the offers or decisions of any other patient.
3 CLEXNET
We introduce CLEXNET causal cissal-expllan wxppanation-guiiieeeeee acceptance model that jointly tackles three challenges:
(i) Predictive accuracy the (biased) training distribution;
(ii) Causal robustness, i.e. invariance to the organ-allocation mechanism that generated the data; and
(iii) Faithfulness to refusal reasons, which convey only direction in which features must change for an organ be accepted.
To achieve these goals, the network couples adversarially-balaneee representation with direc- tional ipplannation-uudee augmentation loss. Figure 3 gives a bird's-eye view of the CLEXNET architecture and Algorithm T details the training loop.
'CLEXNET components that correspond with our main contributions marked with blue

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M(-)
R
Y
LACE
fr
fg
Pp
LEXPL
P
LCLEX
c(+)
LCE
Figure 3: Architecture of our CausaL, EXplanation guided organ-offer model (CLEXNET).
Inputs X, O go through shared representation Φ, which feeds both a clustering head cæ and an acceptance head . The observed refusal category R is embedded ö and combined with X and O before passing it to Lø., All three losses are aggregated into LCLEX. The plus operator with the dashed outline indicates where generated counterfactual edits injected during training. We use blue to represent components related to representations, gray for simple vector operators, purple for losses, pink for loss multipliers, yellow for components that only present during training and green for model inputs used at inference time. The components that correspond with our main contributions are circled with dashed outlines.
Balancing away confounding bias. Under the observed allocation rule Tabe, patients receive offers that are not independent of their covariates. Simply minimizing the binary-cross-entron on such data would entangle acceptance with the policy's selection shortcuts [52 50] In this setting, organs sparse and complex high dimensional objects. Following previous work [8], utilize organ clusters 4 (O), i 1.... k to reduce the dimensionality serve as surrogates for treatment in the balancing step. We aim at adversarially learning [22 13 g an intermediate representation & which is invariant to the propensity of the cluster of the provided organ.
the treatment space. These clusters
Consider an acceptance model CLEXNETO,,9. Op with parameters Gg. fy and Op such that the balanced representation ø = La, (X, O), the organ cluster = co, (ø), and the acceptance probability
Ý Yoy (Ø) are learned jointly.
Using these parameters, we construct a Cross Entropy (CE) loss component for the organ cluster and Binary Cross Entropy (BCE) loss for the offer acceptance probability:
N
Ccr(0s,0p) 0p) = N
[[4 = c(O.)] [(),]]log((E))
(4)
i=1
N
Lnc =
Y log((g)))) + +1 (1-11)))
Y,) -11-Y)) log(1 Y3)
(5)
N
We minimize BCE on the acceptance head and maximize cross-entropy error on the cluster head via a gradient-reversal layer [22 []. Concretely, letting E [0, 1] control the trade-off:
Cacr(0s. À F. 0p)
(6)
Accuracy
uninformaiive about
where the multiplier À serves to control between balancing the representation ø and acceptance prediction. Balancing guarantees that, conditioning on é, the empirical distribution of organ clusters resembles randomised trial, thereby attenuating selection bias [50] [3]. At this point, & can be balanced representation, but the refusal explanations are not yet being used.
Embedding reasons. Besides predicting Y and being balanced, we also aim for CLEXNETé,,0 O0, able learn from the refusal reasons, they must first be
to respect the given refusal reasons. embedded in X. A fixed lookup M maps every reason to signed vector and is used to transform each observed quadruplet into (X,, O,, Y; M(r)) (X; O, 6.). Negative, zero, and positive entries of ò; respectively indicate whether to decrease, hold, increase the associated feature.
5

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Algorithm 1: CLEXNET: single-instance training step with explanation-guided augmented loss Input: instance (X, O, Y, R); clusters c(.); lookup MM.);
domain F; weights X, edits M; threshold Per
D forward pass of factual Init: encoder Po25 acceptance head Yo cluster head CO2
Φ 4- 48 (X, 0); y + -or (o)());; co, (Ø);
LACE + BCE(Y, Y); LCF + C)(
b derive losses initialize explanation loss
LEXPI. +- 0;
= and R + 9 then
8 +- (R))
embedded reason
for m (X(m) (m)) - Sample (XX F.)
.M do
> guided augmentation (Eq.
plm) t- You fo (X ((m) ((m)))
forward pass of counterfactual
Pmax maxm m (m)); Pavg MI Em p(m);
and avg of counterfactuals
if Piax < Pez then
L LEXPL* + BCE (CE(pavg: Pex);
guarded explanation loss (Eq 7 8
LOLEX + LBCE + + PLEXPL (0s, By 0p} + Update (CLLE)))
> optimization step (optional)
formulate an explanation loss component that is protected by a logical guard such that (i) the explanation loss is only considered for negative samples and (ïi) the model does not already respect the explanation:
Guarded explanation-guidde augmentation. Relying Assumptions D and 2
G = 1[Y; = 0] II max Yor (Do, (X; axx 8x. O, + do.) S Pex)]; a,EA.
CLEXNET's Assumption
where Per as a threshold that should be met by at least one augmented sample, offering tunable relaxation of Assumption [ For each negative example we draw M step-size vectors a; E A, inside a pre-defined feasible set JF (Assumption 2 and form augmented samples. If none of those augmented samples already scores above the target probability Pez. the network is penalized with an additional explanation loss:
N Σ
(y) =
9, BCE( avg a.EA,
Yor (Po, (X, + axx 6x, O, + ao, 50.)) Pex)
N
CLEXNET's counterfactual countrfalan edits
(8)
This loss ensures that when changing the inputs following the given refusal reason, the model will predict an acceptance. The full loss combines representation balancing (Equation ( and explanations:
LOLEX (0s, 0y == LBCE(Os. 0r) A Lce(Os,0p)) + p. LEXPL(09 Oy 0p)
(9)
CLEXNET's cLexNer''explanator guidance
where the multiplier p E [0, 1] serves to control between balancing the representation 6, acceptance prediction and respecting the refusal reasons. CLEXNET'S optimal parameters are then given by:
0 == argmax LOLEX 0g, and (0%, 05-) = argmin LCLEX 0r, 0g
(10)
b
Finally, these parameters are learned by optimizing LCLEX By, 0p) using stochastic gradient descent-based optimization and by placing gradient reversal layer before the clustering head ce An instance-leve training loop is shown in Algorithm T which deviates from a standard training loop in the case of a negative instance with provided refusal reason.
Intuitively, the adversarial term removes information that stems from Toka, while the explanation term injects causal directionality unavailable from labels alone. The two components thereby complement each other: without balancing. the model would learn confounded shortcuts; without explanations, would be free to satisfy the loss in any direction, including biologically implausible ones.

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Implementation notes. In Equations 7 and 8 A represents the domain of feasible edit magnitudes for specific instance. While A, depends on X, and O, a more general feasible region F C & U can be defined beforehand which can then be used to find A, for every instance. Note that J need not coincide with the entire domain that would be explored in gold-standard randomized controlled trial (RCT) [26]: practitioners are free to tighten the feasible region F by excluding biologically or logisti- cally implausible edits based on their domain knowledge. Instead, our implementation constructs hyper-box whose bounds are the per-feature minima and maxima observed in D. Consequently, A is then constructed feature-wise for each instance. Finally, a random sampling method used to sample M step-sized a, vectors.
The confidence in the explanations represented by Pez: if there exists a sample along direction d that achieves acceptance probability Pr.., then LE becomes zero for that instance, otherwise, all sampled instances trigger a higher LEXPL. component.
Related work
ML for organ allocation. Current U.S. match-runs still hinge on simple urgency scores most prominently MELD (39] and MELD-Na 34 -tht ignore how an organ's quality, competing patients, and logistics jointly shape long-term benefit. Motivated by these limitations, growing line of allocation rules. Causal policies rank candidates by estimated
work designs Machine-learningdiie individual treatment effect or counterfactual survival 43 66
g 6 aiming to disentangle medical need from policy-induced confounding. second stream enriches the objective with operational constraints such as transport distance, fairness and cold-ischemia limits, turning allocation into combinatorial optimization problem, with often no closed-form solution I 9 45 40). More recent hybrids tackle both aspects simultaneously, coupling causal value estimates with stochastic models of future organ and patient arrivals so that present-day decisions account for tomorrow's s opportunities [ 6 77). However, apart from one approach [40), no prior work has embedded an acceptance estimator within the allocation policy itself-let alone one grounded in a causal framework.
65
Learning with domain knowledge. While not being mutually exclusive, learning from the observed refusal reasons differs from injecting expert knowledge inductive bias into model: the former empirical, data-driven labels or features obtained from real clinical decisions, whereas the latter entails imposing pre-defined domain rules structures (e.g. hard-coding donor age cutoff penalty) into the learning process instead of letting the model discover such patterns from data 117 66) While incorporating domain knowledge can improve sample-efficiency, generalization and interpretabiliity it introduces notable challenges. Eliciting, formalizing and maintaining expert rules is costly and time-consuming the long-recognized "knowledge-acqquiition bottleneck" of expert systems 53 32 [4]. Moreover, relying on domain knowledge risks embedding incorrect or outdated assumptions: if an expert's belief is flawed, encoding it can mislead the model and remain hidden until it causes failures 38 [6]. Consequently, leveraging the recorded refusal reasons-groundee in actual outcomes offers a complementary and often safer causal signal, whereas expert-derived inductive biases must be applied judiciously and subjected to continuous post-deployment validation.
31 (3)
5 Experiments
Experiments on the choice of the feasible region F. additional results for Experiment 5.1 and findings regarding empirical support for Assumption [ can be found under Appendix A More
detailed information about experimental setups, synthetic functions, generation of refusal reasons and used features can be found in Appendix B Information regarding hyperparameters and an acknowledgments section can be found in Appendix
All code, synthetic generators and an implementation of CLEXNET are made public to facilitate independent assessment: hhttp::/ggtthiii wwttchuu..ommmmiii cotp://eesaadtollrccheaaeaCeeeee
5.1 Does CLEXNET generalize better?
Experimental setup. evaluate the performance of different acceptance estimators we create two synthetic datasets that consist of patient-organ offer pairs: Dobe and D), where Dobe represents the
7

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observational dataset which is affected by selection bias and D represents an unbiased dataset with random patient-organ pairs, which is used to test the models in an unbiased way. Dons then split further into Drrain, which is used to train the models, and 'test, which is used to test the models on observational data. We evaluate our model on both synthetic and semi-synthetic experiments.
For the synthetic evaluation, we construct a synthetic binary outcome function following a sim- ilar methodology to previous work in individual treatment effect estimation g
2
27
30
52
fy(X,0) T+rexp (k(X which is set such that the average acceptance probability corresponds with a chosen acceptance
where ((X, O) is a random non-linear function and e is scalar
ratio. Y is then sampled from a Bernoulli distribution with probability (y(X, O). Finally, refusal reasons generated for all negative samples by finding the direction of the necessary edit such that fx + axxx. O + aooo) reaches at least some predefined probability Pez.
For the semi-synthetic evaluation, we use UNOS-PTTS [.5] liver offers recorded between 2021 and 2024. This data consists of approximately 1.1M offers made between 24k unique organs and 46k unique patients. We considered refusal reasons regarding donor age and cold ischemic time. The used patient and organ features are shown in Appendix
Benchmarks.
The performance of CLEXNET is compared against other traditional ML estimators from previous work (33 and used in official simulators [54] [55 (56] 18] [1]. We also compare
CLEXNET against other neural estimators: a single-task adaptation of PATIENTNET ORGANITE model [ adapted for binary classification.
40 and an
Table 1: Performance metries on acceptance. Models are trained and tested on both synthetic and semi-synthetic datasets. BCE, AUC, AUPRC and Brier score are evaluated on the test sets D). Standard deviations are instance-basse and are shown in brackets. Models are ranked from least to most complex. Performances the observational test sets can be found in Appendix A
Synthetic data
Semi-synthetic UNOS-PTR data
Model
BCE
AUC
AUPRC
Brier
BCE
AUC
AUPRC
Brier
Logistic Regression
33
.840 (AR5)
.97
.166
.298 (254)
1,265 (.56)
.540
.279
.27 (wm
Random Forest [33
.543 (313)
.737
.889
.182 (129)
2.195 (6
.536
.268
.26 ca0m
PATIENTNET 40
784 (1,25))
.799
920
.20 (355)
828 (1:348
.593
.308
.23 (3m)
Balancing
Explanations
ORGANITE 8
.42 (690)
.840
.922
.140 (234)
8855 (1.42))
.598
.308
.235 (422)
Explanations
CLEXNET (A 0)
.427 (.99)
.845
.938
.132 (363)
.539 (487)
.655
.355
.181 (190)
CLEXNET
.377 (690)
.872
.948
.117 (230)
.514
.704
.408
.170 (200)
The results are shown in Table The single-task PATIENTNET, which ignores treatment- specific balancing, struggles to generalize beyond the biased training distribution, whereas OR- GANITE's s domain-invariant design recovers a sizeable performance boost. Building on the same balaneing idea while further leveraging direction-only refusal information, CLEXNET consistently tops the table: it achieves the lowest error, the best ranking ability, and the sharpest probability calibration, demonstrating that coupling causal balaneing with explanation-guidee augmentation yields the most transferable acceptance model.
Results.
5.2 Does confounding affect performance?
Experimental setup. We test how robust CLEXNET is under different levels of confounding compared to other models. The same synthetic setup used as in Experiment However, the level of linear bias & is gradually increased meaning that organs in Dobs become more dependent on the corresponding observed patients.
Results. Figure plots performance metrics against an increasing linear confounder level ab. PatientNet is the most vulnerable and reveals substantial loss of accuracy and calibration with increasing bias. ORGANITE CLEXNET are comparatively stable thanks to their balancing mechanism. Their metrics do drift upward/downward as bias strengthens, but the changes are modest and far smaller than those seen for PATIENTNET, showing that balancing-ani for CLEXNET, the additional direction-only efferiiiionneeeeiiiill.lll dampens the impact of confounding.
8

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0.90
0.85
0.6
0.75
0.5
0.70
0.4
0.60
AUPRC
Brier
0.30
0.95
0.90
0.20
0.85
0.80
0.10
0.775
Confounding level
Logistic Reg.
OrganITE
Figure 4: Robustness of CLEXNET to increasing confounding, Linear confounding v is gradually introduced add bias into the patient-organ pairs that present in Dobs- The models tested on Dr. The bands represent 95% bootstrap percentile confidence intervals for each model. More information about the confounding mechanism can be found in Appendix B
5.3 Are all directional reasons equally informative?
Experimental setup. We vary the mechanism that seleets positive counterfactual samples paired with negative observations generate directional refusal reasons 8,, Three mechanisms are compared: (i) random sampling from an unbiased set of feasible patient-organ pairs; (ii) inverse probability weighting (IPW), which reweights samples using kernel density estimate Pobs (X, O); and (iii) boundary intersection method that selects counterfactuals crossing the decision boundary in low- density regions. A separate CLEXNET model is trained the explanations generated by each mechanism.
Results. Even with explanations, the quality of the refusal-reason generation matters (Table 2 Random uniform sampling often samples from within the relatively high-density observational region. Although IPW favors positive counterfactuals in underrepresented regions, does not guarantee that the decision boundary will not be crossed in the high-density observational region. Instead, the boundary intersection sampler tries generate counterfactuals whose directions contain information that is not already contained in the observed distribution. Hence, effort spent on gathering plausible counterfactual directions is repaid in -ittr--disttibution performance.
Table 2: Impact of different reason generation mechanisms on CLEXNET. All metrics are evaluated on the unbiased set Dz. Instance-based standard deviations are shown in brackets.
Reason mechanism
BCE
AUC
AUPRC
Brier
Uniform Random
401 (.752)
.855
.940
.123 (.233)
IPW
.055 (.649)
.841
.930
.126 (.222)
Boundary Intersection
.368 (.750)
.889
.958
.114 (.233)

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Discussion
6.1 Limitations
While CLEXNET closes several gaps in current organ-offer modeling, important caveats remain.
Dependence the refusal-vector mapping M. Our framework assumes predefined mapping from each categorical refusal code to a signed direction vector = M(Rt). . In practice, some refusal reasons are coarse and represent latent features, requiring edits a predefined set of multiple features that define the latent feature (e.g. "poor donor quality"). Any systematic misspecification of M will bias the explanation loss in Equation 8. potentially driving the decision boundary in an implausible direction.
Feasibility region FF and edit set A,, We bound counterfactual edits by a hyper-box whose edges are the Srature-wise minima and maxima observed in Dobe. This choice is intentionally conservative but imperfect: (i) it still permits biologically infeasible edits that are purely numerical outliers and (ii) it omits latent constraints such as blood-type compatibility or size-matching rules that are not explicit features. misspecified FF can either suppress the explanation loss (if too large) or force the model into an unreachable region (if too small; see Experiment A.1). Embedding clinical constraints (or a learned generative prior) into the augmentation sampler remains a challenge.
Senth-JSytthetic evaluation. All quantitative experiments are performed on controlled synthetic or semi- myni-sytthttic data sets whose generative mechanisms match the modeling assumptions (directional explanations refer to unobserved counterfactuals, F can be reasonably approximated by a hyper-box around Dabs. etc.). Real data exhibit additional noise sources: missingness, time-varying policies, and outdated measurements. Deploying CLEXNET in an actual simulator therefore requires (i) auditing its calibration and fairness on historical wait-list snapshots and (ii) stress-testing under counterfactual policy shifts 77]
Distributional changes are likely. The data-generating process that underpins CLEXNET is not static. Organ supply trends, recorded patient data, refusal codes, clinical practices, and policy rules all evolve over time, leading to exogenous distribution shifts. Moreover, once a model-based allocation policy is implemented, clinicians may change their acceptance behavior in response to the new incentives (performative prediction) phenomena erode calibration, bias subgroup performance, and invalidate causal assumptions if left unchecked. Routine drift detection, scheduled re-evaluation of the refusal-vector mapping M, and prospective shadow evaluation fresh wait-list data are therefore mandatory safeguards before, during, and after deployment.
endogenous shift created by the model itself [46]. Both
While the first two limitations are CLEXNET-specific and aim to weave richer domain knowledge directly into the architecture; the latter two address long-standing open problems for all causal models Addressing these limitations constitutes an important research agenda before CLEXXNT can reliably inform real-world organ allocation policy.
used organ allocation (66 65 g
6.2 Future work
Embedding richer refusal information. Language-model embeddings could translate each coded or free-text refusal into vector that lives in the same space as patient and donor features. An alignment layer could then connect that vector to the model's gradients, producing soft weights over which attributes should change- extending beyond simple directions and accommodating distance or monotonic hints. Treating the text-to-edit map as learnable replaces the predefined lookup table, opens the doors to free-text refusal reasons and lets uncertainty in explanations flow through training, effectively offering a continuous, instance- based relaxation of Assumption T
Refining the feasible region. A practical edit domain could be built by shrinking the hyper-box to the high-density core of the observed patient-donor distribution and discarding segments that violate hard clinical rules such blood type size compatibility. This density-trimmed, constraint-aware region screens out implausible counterfactuals while keeping unbiased coverage of realistic cases, sharpening the explanation loss and improving data efficiency.
10

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A Additional results and experiments
A.1 What role does J play?
As explained in Section the role of F is crucial for the construction of A. Without F, the model would not know where stop looking for plausible counterfactuals. CLEXNET constructs this feasible region F by storing the maximum and minimum values according to the features on the observational dataset Pobs to create F, resulting in hyper-box. In this experiment, we evaluate how well that works and look at other greedy and conservative approaches.
Experimental setup. The construction of the feasible region F is adapted to allow for its expansion contraction. Let fmin and fuax denote the minimum and maximum observed values of specific feature in Dobs, and let fg be its observed mean. The bounds the hyper-box are redefined feature-wise as:
( : Jn + afmiin Sp)) and [la] :- ʃ4 [f fn).
(11)
where a a scaling factor that expands (a > 1) or contracts (0 < < 1) the region around fu- Thus, by varying CLEXNET is evaluated under different feasible region sizes F((),
Results. The results are shown in Figures and CLEXNET's performance worsens significantly as F is contracted by decreasing a. When F is slightly expanded, the performance mostly remains stable. Thus, underestimating F is worse than overestimating
Intuitively, when F is wrongly contracted, the decision boundary in unobserved regions gets wrong- fully restricted, causing significant drop performance. In Figure for example, the decision boundary would be squeezed between the observational region and JF. Instead, choosing J that is large simply diminishes the explanatory signal in Equation model. However, if F is set too large (such as in Figure 5). the signal from the explanations becomes less useful.
[ rather than wrongly constraining the
ClexNet variants
performance box scaling
BCE
0.55
0.88
0.50
0.84
0.82
0.45
0.40
0.78
Contraction
Espansion
Contraction
Expansion
AUPRC
Brier
0.95
0.15
0.14
0.91
0.13
0.12
0.90
0.11
0.5
0.8
1.0
1,2
1.4
0.8
0.8
1.0
1.2
scaling factor
Figure 5: The effeect of changing F on CLEXNET's performance. Performance metrics on the unbiased test set D,= are shown for CLEXNET with different constructions of F. These constructions are achieved by changing the scaling factor a that expands or contracts the original region F (at o 1, marked by a vertical blue dashed line). The bands represent 95% bootstrap percentile confidence intervals.
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performance (log scele)
BCE
AUC
0.88
0.06
0.82
0.80
0.78
0.76
Contraction
Expension
Contraction
Expension
AUPRC
Brier
0.95
0.94
0.93
0.14
0.12
0.90
10a1
10a
10A 10'
10a
10*
10a
10
scaling factor
Figure 6: The effects changing F by magnitudes on CLEXNET's performance. Performance metrics on the unbiased test set Dy shown for CLEXNET with different constructions of F. These constructions are achieved by changing the scaling factor that expands or contracts the original region F (at 1, marked by vertical blue dashed line). The bands represent 95% bootstrap percentile confidence intervals.
A.2 Observational test set performance
Results on the observational test set Diest from Experiment 5.] are reported in Table The results show that PATIENTNET and ORGANITE perform better on the synthetic observational test set as they are less constrained than CLEXNET. On real observational data, Random Forest outperforms the other models. However, shown in Table CLEXNET is able to generalize significantly better on the unbiased test sets Dy-
Table 3: Performance metries on acceptance (observational test set). Models trained and tested on both synthetic and semi-synthete datasets. BCE, AUC, AUPRC and Brier score are evaluated on the observational sets Drest- Standard deviations are shown in brackets.
Synthetic data
Semi-synthetie UNOS-PTR data
Model
AUC
AUPRC
Brier
BCE
AUC
AUPRC
Brier
Logistic Regression 333
.707
.590
.558
.254
.101 (547)
.826
.102
.044 (110))
Random Forest 33 10
.359 (34))
.947
.107 (14))
.107 (994)
.867
.164
.022 (112)
PATIENTNET
.229 (194)
.970
.063 (294)
.118 (479)
.052
.024 (140)
ORGANITE
.243 (299)
.977
.068 (114)
.116 (405)
.640
.052
.024 (114)
CLEXNET (A CLEXNET
.236
.962
.911
.066 (195)
.158
.626
.042
.032 (12
.275
.970
.958
.076 (129)
.159
.598
.036
.030 (123)
A.3 Empirical support for Assumption] 1
Although CLEXNET relaxed version of Assumption T it is possible test empirical support for this assumption in the real data.
Experimental setup. To test support for Assumption T we to match each refusal, with corre- sponding refusal reason, similar acceptance that i) satisfies that reason and ii) within a certain range of the refusal.
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Table 4: Support for Assumption versus allowed matching range. Percentages shown the cumulative support for donor-age mamor-age-relatee related reasons, bodd--schemi-time-rre reasons, and overall, the maximum Euclidean daclidean-distance threshold increases.
Allowed matching range (Euclidean distance)
Support for donor age related reasons
Support for cold ischemic time
Overall Support
related reasons
0.1%
0.1%
0.1%
2
2
7.0%
4.7%
6.2%
<
3
71.7%
68.8%
70.7%
14
4
98.7%
97.8%
98.4%
99.9%
99.8%
99.9%
IA
6
100.0%
100.0%
100.0%
Results. If we allow for matching refusals with acceptances within Euclidean distance of (considering that the feature space has 76 dimensions after OHE), we can find suitable positives that satisfy Assumption [ for over 70% of the observed refusals with reasons related to donor age cold ischemic time.
B Experimental setups
B.1 Experimental setup for synthetic studies
This section spells out every stochastic component, hyper-parameter and practical decision that enters the construction of the synthetic data used throughout all experiments. Re-implementing the pipeline line-by line-by-line should therefore reproduce the raw data on which CLEXNET and the baselines were trained and evaluated.
Notation recap.
A single observation is a quadruplet (X, O, R) where
patient covariates (demographic, clinical, logistical), O e Rdd organ-offer attributes (donor and procurement information),
X E d
Y {0, 1] acceptance indicator, 1 represents accepted,
R E R categorical refusal reason when Y 0.
We fix d, = d, in all experiments.
B.1.1 Generating patient covariates X
Patients are sampled i.i.d. from standard multivariate normal:
X (a,)
(12)
This choice deliberately avoids introducing any implicit structure; all correlations subsequently arise from the confounding mechanism.
B.1.2 Generating organ offers O with tunable confounding
To model the clinical intuition that organs not allocated independently of the candidates to whom they are offered, we introduce a linear confounding parameter vb E [0, 1) and draw
O +
/ 11--8 Lize
E ~~ Lo,, Apg No,
(13)
When d = 0 organs and patients are independent; as v + they become almost deterministically aligned via random matrix A (Experiment 5.2 sweeps across 15).
Moreover, an additional, non-linear bias is added by following the procedure:
1. draw i..d. pool of No candidate pairs (X, O) using Steps B.I. B.I.2 (with
0);
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2. pass every pair through random, non-linear function g(X, O) (randomly initialized, frozen thereafter) and retain the scalar score s; = g(X,, O,);
3. keep only the top 5% and bottom 5% of pairs by 8;.
The retained 10% constitute the observational data set:
Dots ((X,, O.) 8; in extreme decile), := |Dossl 0.1No.
Train/test split. We allocate 70% of Dobs to Dirain. 15% for validation set and 15% to Drest (stratified by Y).
B.1.3 Ground-truth outcome mechanism fy
Acceptance probability a random logistic transformation of frequently used scoring function with
an additional interaction term 8 2 27 30 52] :
|X 0) = w1 w20 W,O,
(14)
(v
(15)
1 vexp (h X, 0)) +
Here, and represent random vectors, and W3 E Rd is matrix with random entries. The scalar chosen such that [fyl 0.50, making acceptance a balanced label. Finally, outcomes sampled such that Y Bernouuli(Sy O)).
B.1.4 Generating direction-only refusal reasons R
For every negative instance = we synthetically attach a direction ó rather than an absolute counterfactual. The procedure is follows:
1. Draw an auxiliary set Dÿ of (X', , O') pairs without the bias.
2. Keep only those candidates with fy (X', 2 Per (default Per = 0.5).
Uniformly sample one such "positive" and compute (X' - X, O' - O), then map 6 onto based on the sign of each element.
4. (Optional) Map å to categorical refusal label R via lookup table M (many-to-one). The last step is bypassed in the experiments and 5 used directly. The model never sees magnitude information since delta mapped onto A.
Feasible domain F. We store the per-feature minima and maxima over all draws (before selection). Sampling magnitudes E A; then clamps each edited feature to this hyper-box to respect Assumption
2
B.2 Experimental setup for semi-synthetic studies
Liver offers from the UNOS- PTR 11 dataset, recorded between 2021 and 2024, are used for the semi-synthetic evaluation of CLEXNET. Following previous work (33). only offers related to organs that eventually got placed are considered. resulting in approximately 1.1M offers. The considered features are shown Table For the construction of Drr a separate CLEXNET model was trained on the observational data (including the refusal reasons) and used as (X, Next, real patients are paired with real organs to generate Dyr This way, the real covariate structures of patients and organs are preserved.
B.3
Compute resources
Experiments ran on 13th Gen Intel(R) Core(TM) 19-13900HX processor with 32GB RAM. End-to-end wall-clock time: training CLEXNET &s 60s run, including the in-the-loop explanation- guided augmentation (Algorithm
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Table 5: Considered features from the UNOS-PTR dataset 15
to represent patients and organs.
Patient Features
GENDER
recipient gender
DAYSWAIT DAYSWAITCCHRON CCRRON
days on liver waiting list
ETHCAT
recipient ethnicity category
INIT AGE
age in years at time of listing
INIT ALBUMIN
initial waiting list albumin
INIT ASCITES
initial waiting list ascites
INIT BMI BML_ALC CALC
calculated candidate bmi listing
INIT BILIRUBIN
initial waiting list bilirubin
INIT INR
initial waiting list inr
INIT SEEUM
CREAT
initial waiting list serum creatinine initial waiting list serum sodium calculated recipient height (cm) calculated recipient weight (kg) recipient primary diagnosis
SERUM_SODIUM
HGT CM CALC
WGT KG CALC
DIAG
Organ Features
AGE DON
donor age (yrs)
ALCOHOL HEAVY_EEAOOO
ddr heavy alcohol use (heavy= 2+ drinks/day) (y/n/u) donor bmi pre/at donation calculated
BMLDON CALC
COD_CAD DON
deceased donor-cause of death
COLD ISCH
total cold ischemic time (hours)
ETHCAT DON
donor ethnicity category
HGT_CM BL__CMMDOO DON _CALC
_ON__ALCC
calculated donor height (cm)
HIST CANCER_NCCOOO DON
deceased donor-history of cancer (y/n)
deceased donor-history of cigarettes in past (more than 20 pack yrs)
GENDER DON
donor gender
deceased donor-non-hean. heart beating donor
C CLEXNET's hyperparameters
The hyperparameters that were used for CLEXNET in the experiments can be found in Figure 7 The same hyperparameters are used for tb (X, O). Yoy (Ø) and co (Φ) across all neural network based models. Following previous work [8) the organ clustering function c(.) and thus the organ clusters e; are determined by a k-means clustering algorithm.
Two studies shown in which novel hyperparameters varied:
1. In Table 6 variations of CLEXNET with different values for À and shown. These parameters control the trade-offs between predictions on the observational data, represen- tation balancing and respecting explanations. Both parameters significantly impact the performance on Dr.
2. On Figure 7 variations of CLEXNET with different values of M are shown. This parameter controls how many augmented data points need to be sampled in the training loop (Algorithm
Th for each instance with refusal reason. The results show that the benefit of increasing M flattens after certain magnitude is reached (M 100 in this case).
19

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Table 6: CLEXNET'S performance with different loss weights. CLEXNET variants shown with different weights for loss components À (for representation balancing) and p (for explanatory supervision). The blue cells corresoond to the results from Experiment 5.1 calculated using bootstraps and are shown in brackets.
Standard deviations
BCE Dieer
BCE 2y
Alp
0.05
0.10
0.15
0.20
A
0.05
0.10
0.15
0.20
0.05
.225 (.027)
.231 (.026)
.236 (.028) .243 (.034)
0.05
.431 (.008)
(.009)
.419 (.009)
.446 (.009)
0.10
243 (.02))
2250 (.025)
.255 (.029)
.248 (.024)
0.10
.405 (.007)
.411 (.007)
.396 (.007)
.3% (.009)
0.15
.766 (.026)
.281 (.027)
.255 (.031)
.281 (.052)
0.15
.405 (.007)
.405 (.006)
.377 (.007)
.384 (.007)
0.20
284 (.032)
.293 (.030) .2%6 (.027)
.297 (.025)
0.20
.300 (.007)
.383 (.007)
.392 (.008)
.355 (.006)
AUROC Drest
AUROC Dy
A
0.05
0.10
0.15
0.20
A
0.05
0.10
0.15
0.20
0.05
.78 (.010)
973 (,000)
969 (.013)
0.05
841
(.004) .880 (.004)
866 (,004)
861 (.004)
0.10
977 (.010)
(.010))) 975 (,012)
976 (.011)
0.10
854 (.004)
844 (.004)
865 (.004)
.873 (.004)
0.15
974 (.01))
972 (.01)) 970 (.014)
966 (.015)
0.15
845 (.004)
841 (.005)
872 (.004)
.83 (.004)
0.20
965 (.016)
(6015
967 (.014)
.73 .73 (.012)
0.20
865 (.004)
.669 (.004) 860 (.004)
853 (.04))
AUPRC on Drest
AUPRC on D3
A
0.05
0.10
0.15
0.20
A
0.05
0.10
0.13
0.20
0.05
976
972((011) (.02))
970 (.013) .96 (.017)
0.05
.922 (.003)
.953 (.002)
.977 (.002)
.944 (.002)
0.10
975 (.012)
973 (,012)
972 (.015)
.922 (.013)
0.10
940 (.003)
.937 (.003)
.455 (.002)
.90 (.00))
0.15
968 (.016)
967 (.017)
.588 (.024)
.957 (.022)
0.15
933 (.003)
.911 (.003)
.488 (.002)
.488 (.002)
0.20
949 (.030)
947 (.027)
953 (.026)
965 (.021)
0.20
943 (.003)
945 (.003)
941 (.003)
937 (.003)
Brier on Dicat
Brier on D
AVp
0.05
0.10
0.15
0.20
A1P
0.05
0.10
0.15
0.20
0.05
.066 (,011)
.069 (.010)
.069 (.011)
.071 (.012)
0.05
111 (.003)
.119 (.002)
.123 (.00))
.128 (.003)
0.10
068 (.009)
.071 (.010)
.073 (.011)
.770 (.009)
0.10
.25 (.002)
.127 (.002)
.121 (.002)
.120 (.002)
0.15
078 (.010)
.079 (.010)
.076 (.011)
.78 (.011)
0.15
116 (.002)
.127 (.002)
.116 (.002)
.118 (.002)
0.20
080 (.011)
.083 (.011) .083 (.010)
.83 (.010)
0.20
120 (.002)
.118 (.002)
.121 (.002)
.123 (.002)
ClexNet variants performance vs. augmentation batch M (log scale)
0.06
0.84
AUPRC
Brier
0.99
0.145
0.100
0.135
0.93
0.125
0.220
0.115
0.30
augmentation batch size
Figure 7: CLEXNET'S performance for different augmentation batch sizes. Performance metrics on the unbiased test set D are shown for CLEXNET with different values for the augmentation batch size M. The default value used in other experiments is set M 100 (marked by vertical blue dashed line). The bands represent 95% bootstrap percentile confidence intervals.
20

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Table CLEXNET's Hyperparameters
Component
Hyperparametens
Shared Encoder Do (X, O)
Dense(32, L2).
ReLU Activation
Dense(32, 1.2), ReL.U Activation
Acceptance Head Yor (0)
Dense(32, L2).
Sigmoid Activation
Organ Cluster Head
Dense(32, L2). ReLU Activation
(o)
Organ Cluster Amount
3
k
Organ Cluster Loss Weight
0.15
Explanation Loss Weight
0.15
Augmentation Batch
100
M
Training Parameters
Maximum Epochs: 1000
Patience: 30
Learning Rate: 10-3
Acknowledgments
The organ-patient pair data reported in this work has been supplied by the United Network for Organ Sharing as the contractor for the Organ Procurement and Transplantation Network. The interpretation and reporting of the data the responsibility of the author(s) and in way should be seen as official policy of or interpretation by the OPTN or the U.S. Government.
21

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28


Reviews:
[{"review_id": "7XyBliqIU4", "reviewer": "Reviewer_cg51", "summary": "The 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.", "questions": "* 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?", "limitations": "The lack of real world data is not discussed.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### 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.", "was_revised": true, "final_justification": "The authors addressed my biggest concerns, and I think they are significant. Therefore, I change my review from 3 to 4 and quality score from 2 to 3.", "rebuttal": ""}, {"review_id": "BpLFUVOuEP", "reviewer": "Reviewer_QPcZ", "summary": "This 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.", "questions": "1.  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?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "lw68RhG7iY", "reviewer": "Reviewer_AejF", "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.", "questions": "None", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n- 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.\n- 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.\nThe presentation is clear, with easy-to-understand motivation and modeling details, for a reader not familiar with this topic to dive in.\n\n**Weaknesses:**\n- 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.", "was_revised": true, "final_justification": "The rebuttal addresses concerns and I keep my original positive rating.", "rebuttal": ""}, {"review_id": "26jA0w2S4d", "reviewer": "Reviewer_iTSa", "summary": "This 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.", "questions": "- 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?", "limitations": "yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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\"", "was_revised": true, "final_justification": "My main concern, about experiments with only synthetic datasets, has been addressed. Authors also addressed the weakness about it not being clear which parts are novel. I have raised my score accordingly.", "rebuttal": ""}]

Author rebuttal:

Meta-review: (a) Scientific claims and findings
The paper introduces CLEXNET, a framework for modeling transplant offer acceptance. Unlike prior models that rely solely on observational data, CLEXNET reframes the task as counterfactual prediction and leverages categorical refusal codes as direction-only signals. Synthetic experiments show improved generalization and robustness compared to prior approaches.

(b) Strengths

Compelling motivation addressing an important problem in organ allocation.

Clear formulation and well-presented method.

Novel use of refusal codes as direction-only counterfactual signals.

Innovative integration of adversarial training with a contrastive explanation loss.

Demonstrates improved robustness and generalization in synthetic experiments.

(c) Weaknesses

Initial results limited to synthetic datasets, raising questions of real-world applicability.

Assumptions about the reliability and informativeness of refusal codes may not hold in practice.

Lack of human/clinical validation.

Unclear delineation of which parts of the method are novel.

(d) Reasons for decision
I recommend acceptance. The paper presents a novel and well-motivated framework addressing an important, underexplored issue in organ allocation. Despite initial concerns about evaluation on only synthetic data, the rebuttal addressed this by providing results beyond synthetic datasets, which reassured reviewers. The theoretical and methodological contributions, together with the relevance of the problem, justify acceptance.

(e) Rebuttal and discussion
During review, concerns centered on the reliance on synthetic datasets, unclear novelty of some components, and assumptions about refusal code quality. In rebuttal, the authors presented additional results beyond synthetic data, alleviating the primary concern. Reviewers were satisfied with these clarifications, and consensus shifted positively, leading to the final recommendation of acceptance.
Decision: Accept (poster)