Class-Aware Reinforcement Learning for Counterfactual Explanation Generation

Mary-Anne Williams; Muhammad Adil Saleem; Syed Ali Raza

REVIEW 3 major objections 5 minor 300 references

Class-Aware Reinforcement Learning for Counterfactual Explanation Generation

T0 review · 3 major / 5 minor · reviewed 2026-07-31 · deepseek-v4-flash

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Pith's one-line read This paper argues that including the instance's predicted class in the reinforcement-learning state representation makes counterfactual explanation generation faster, higher-reward, and more often valid.

desk verdict A genuinely useful empirical observation about RL state design for CFE, but the main claim needs a control for added input dimension before it's fully convincing. read the letter →

arxiv 2607.27905 v1 pith:WKZJW3GW submitted 2026-07-30 cs.LG cs.AI

Muhammad Adil Saleem , Syed Ali Raza , Mary-Anne Williams This is my paper · ORCID

classification cs.LGcs.AI

keywords counterfactualexplanationsreinforcementlearningclass-awarestatePPOSHAPLIMEtabulardatavalidity

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Counterfactual explanations (CFEs) are 'what would need to change' instances that flip a black-box model's prediction, and this paper studies how to generate them with reinforcement learning. Its central claim is that adding the instance's predicted class to the RL state—alongside the usual feature values—improves the search. Across seven tabular datasets, the class-aware policy converged faster, reached higher terminal rewards, produced shorter episodes, and generated significantly more valid CFEs than a class-blind policy identical except for the missing class input. The paper also reports that the class feature ranks among the most influential inputs to the policy's action choices according to SHAP and LIME. If correct, this offers a simple, zero-cost change to many RL-based CFE systems: use a prediction that is already available.

What carries the argument

The load-bearing object is the MDP state used by a Proximal Policy Optimization agent (a standard clipped policy-gradient method). The state is the vector of transformed feature values plus one extra immutable entry: the predicted class of the current instance from the black-box classifier. Actions are single-feature increments or decrements chosen from a set of size twice the number of mutable features; transitions are deterministic and terminal rewards are computed only at episode end as +10 for a valid flip, minus penalties for sparsity and cosine-distance proximity, with a -1 timeout penalty. This state design is what carries the argument: it gives the policy a direct, always-up-to-date

What would settle it

Train the class-blind policy with an extra immutable state entry filled with a random or constant dummy value, keeping input dimension and network capacity the same. If this dummy-feature policy matches the class-aware policy's convergence and validity, the causal role of class-awareness is refuted; if it performs like the original class-blind policy, the class content matters.

Watch

Extended reading notes

Core claim

The discovery the paper aims to establish is that class-awareness is not just neutral but beneficial: a reinforcement-learning agent that observes the current predicted class of the instance it is editing becomes a better counterfactual generator. The authors set up counterfactual generation as an episodic Markov decision process in which an agent changes one feature at a time until the black-box model's prediction flips, with terminal rewards for validity, sparsity, and proximity. The proposed class-aware state representation appends the model's prediction for the current state to the other features; the class-blind baseline is the same formulation without that input. On seven tabular datas

Load-bearing premise

The assumption that carries the paper is that the improvement comes from the class information itself; the experiments do not control for the extra input dimension, and because the class is a deterministic function of the other features, the added state entry provides no new information.

Editorial extensions

If this is right

RL-based CFE systems can incorporate the predicted class at zero additional data-collection cost, since the prediction is already computed for the instance.
Higher validity and shorter episodes mean faster, more reliable recourse generation for large tabular datasets and production black-box models.
The observed gains in both training convergence and test-time validity suggest class-awareness may help in other RL search tasks where a goal condition is cheaply computable.
Feature-importance analyses of RL policies should include the class input when it is present; otherwise they will miss a consistently top-ranked driver of action selection.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because the predicted class is a deterministic function of the other features, the class-aware state carries no new information in the information-theoretic sense; the benefit likely comes from making the goal condition directly visible and from the extra input dimension, so a dummy-feature control would separate these explanations.
A testable extension: train class-aware policies and then distill them into class-blind policies of the same architecture; if the distilled policies keep the validity gain, the class input matters only during learning and can be removed at deployment.
The same recipe—append the current model prediction to the state—could transfer to image or text counterfactual generation, where an agent editing pixels or tokens could similarly benefit from a direct 'am I done yet?' signal, though the paper does not test this.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper tests a simple idea: add the classifier's predicted class to the RL state when generating counterfactual explanations. That's genuinely new in this literature, and the comparison on seven datasets is more thorough than most. The strongest result is the validity improvement (p=0.0071), and the training curves are consistent with faster or better convergence for the class-aware agent. I'd call that a real empirical pattern.

Where it falls short is the interpretation. The predicted class is a deterministic function of the other features, so the class-aware state carries no new information—it just gives the policy network an extra input dimension and the first layer extra parameters. The paper never runs a dummy-feature or capacity-matched control. Without that, you can't attribute the gain to 'class-awareness' rather than to having a wider input. The SHAP/LIME analysis doesn't help: the policy obviously uses a feature that's in its state, and the feature's top ranking just says it correlates with actions. So the stress-test note is correct: this is a load-bearing confound for the paper's central claim, and it's fixable with one control experiment.

Other soft spots are minor by comparison. The convergence-speed claims are visual; no statistical test or timing. Table 3 doesn't show an 'ours' column, which makes the benchmark comparison harder to read than it should be. The code is promised but no working link or verification. None of these are fatal.

Bottom line: the paper deserves a serious referee. It's a modest but useful contribution, and the validity result is worth knowing even if the mechanism is underspecified. What I'd ask the authors for in revision is the dummy-feature control, a capacity-matched baseline, and some error bars or tests on convergence. I'd bring it to a reading group as an example of a common RL-for-XAI pitfall, but I wouldn't treat the class-awareness effect as established until the control is done.

Referee Report

3 major / 5 minor

Summary. The manuscript proposes adding the predicted class label of the input instance to the state representation of a PPO-based reinforcement learning agent that generates counterfactual explanations (CFEs) for a black-box classifier. The method is compared with a class-blind variant on seven tabular datasets. The authors report faster convergence, higher rewards, shorter episodes, and significantly higher CFE validity for the class-aware variant (p=0.0071), and use SHAP and LIME to show that the class feature is among the most important in the policy's action selection. The paper claims that class-awareness causally improves CFE search.

Significance. The proposed modification is simple and easy to adopt, and RL-based CFE is an active area; demonstrating a reliable benefit from including the predicted class would be a useful practical contribution. The paper's strengths include public datasets, provided code, and a significance test for the validity difference. However, the central causal claim is currently not established because the design confounds the semantic content of the class with an additional input dimension and policy capacity, and the convergence claims are not statistically tested.

major comments (3)

[Sections 3.1–3.3 and 5.2] The design misses a dummy-feature control. The class-aware state includes one extra input dimension, so the PPO policy has a larger first layer. Since the predicted class is a deterministic function of the other features (given the trained XGBoost model), it carries no additional information; the observed improvements in validity, reward, and episode length could be due to the extra capacity or to a generic beneficial effect of an extra input. To attribute the effect to 'class-awareness', the authors should add a control with a random/dummy feature of the same scale, or match capacity (e.g., an extra unused input in class-blind). Without this, the core conclusion is unidentified.
[Section 5.1] Convergence claims are not statistically supported. Figure 4 shows only mean curves from 10 runs; no confidence intervals, significance tests, or quantitative definitions of 'faster convergence' are provided. The statements that class-aware converges faster in some datasets and slower in Adult Income are based on visual inspection. The authors should report time-to-threshold or final reward/episode differences with confidence intervals and a paired test across seeds.
[Section 5.3] The SHAP/LIME analysis is used as confirming evidence, but it cannot distinguish semantic benefit from the general use of a state feature. A feature that is always present and immutable will naturally be used by the policy, especially in the terminal reward condition. The fact that LIME does not rank 'Prediction' in the top 5 for two datasets also weakens the 'consistently among the top features' claim. This evidence does not address the confound raised in the previous comment.

minor comments (5)

[Table 3] A column for the proposed method would make the comparison easier; currently the class-aware results are in Table 2, so the reader must cross-reference.
[Table 2] Typo in the heading: 'validaity' should be 'validity'.
[Sections 1, 2, 5.3] The text contains the encoding artifact "instance?s" (e.g., in the Introduction and Section 5.3). Please fix.
[Section 4] The sentence "the predicted class-based feature was excluded when computing these measures" is important for understanding the reported sparsity/proximity values; state this earlier and ensure the class-blind evaluation also excludes the same feature.
[Section 3.4] Use consistent notation for the test set: D_ts vs \bar{D}^{ts}.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the class-aware RL comparison is an independent empirical experiment; the missing dummy-feature control is a confound, not a circularity.

full rationale

The paper's central claim is an empirical A/B comparison between two PPO-based RL agents that differ only in whether the predicted class is included in the state representation. The policy is trained and then evaluated on held-out test instances against a class-blind baseline and external benchmarks (DiCE, ReLAX). The predicted class feature is computed by the already-trained classifier M and is not a parameter fitted to the measured validity/reward outcomes, so no 'prediction' is an input by construction. The SHAP/LIME analysis (Sec. 5.3) shows that the learned policy attends to the class feature it was given; this is corroborative rather than a derivation of the improvement, and it does not reduce the central comparison. The paper's genuine weakness—that the class-aware state has an extra input dimension and therefore more first-layer parameters, while the predicted class is a deterministic function of the other features—is an experimental confound undermining causal attribution, not a case of the paper deriving its conclusion from its own inputs. No load-bearing self-citation or imported uniqueness theorem was found; the only self-citation (Williams 2021) is a definitional aside. The result is therefore self-contained and non-circular.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

No new entities are postulated. The free parameters are mostly shared across arms; the main uncontrolled variable is the extra input dimension in the class-aware state, which is not a fitted parameter but a design confound.

free parameters (4)

scalar action step sizes = ±0.01 for four datasets, ±0.1 for others
Chosen 'based on preliminary experimentation' (Sec 4); affects achievable sparsity and proximity, though the same for both arms.
reward weights = +10 validity, -1*(1-sparsity), -1*(1-proximity), -1 timeout
Hand-chosen weights; validity dominates, which the authors themselves note explains sparsity/proximity dips for some datasets (Sec 5.2).
feature resolution = 0.01
Min-max scaled values rounded to two decimals; selected as a trade-off between learning speed and proximity (Sec 3).
PPO and XGBoost hyperparameters = PPO defaults; XGBoost 200 estimators, lr 0.05, fine-tuned max depth/positive weight
Selected based on initial experimentation (Sec 4); not varied across the central comparison.

assumptions (3)

domain assumption XGBoost's predicted class is an adequate black-box oracle for CFE validity.
All validity, sparsity, and proximity metrics are defined relative to this model (Sec 3, Sec 4).
domain assumption Binary classification (with one-vs-all for Penicillin) captures the CFE setup.
The methodology restricts to binary class flips; multi-class problems are folded into one-vs-all (Sec 3).
domain assumption SHAP and LIME feature importance meaningfully describe the learned RL policy's action selection.
Used to infer that the class feature matters in decisions (Sec 3.6); interpreting a stochastic policy with post-hoc explainers is itself an assumption.

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Cite this review

Pith. "Pith review of Class-Aware Reinforcement Learning for Counterfactual Explanation Generation." pith.science (2026). https://pith.science/paper/WKZJW3GW

@misc{pith2026260727905,
  author       = {Pith},
  title        = {Pith review of: Class-Aware Reinforcement Learning for Counterfactual Explanation Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WKZJW3GW}},
  note         = {Machine review of arXiv:2607.27905}
}

read the original abstract

Counterfactual explanations (CFEs) enhance the interpretability of black-box models by generating alternative instances with adjusted feature values that achieve a contrastive outcome. Reinforcement learning (RL) offers a promising approach for CFE generation, enabling efficient exploration of counterfactual instances while ensuring control over key metrics like validity, sparsity, and proximity. Previous studies have formulated RL states exclusively using features derived from the predictors in the supervised dataset. This study explores the impact of including an instance's predicted class, alongside features derived from the predictors, in the RL state representation for generating CFEs. The hypothesis is that class-awareness enhances exploration efficiency and improves policy optimality. We compare the proposed class-aware RL method with the class-blind RL method, which is similar but excludes the instance's class information from the state representation. The comparison was conducted using seven datasets from diverse domains, varying in size. The results show that during training, class-aware RL offers benefits in terms of convergence speed, reward optimization, and episode length reduction. Moreover, it generates significantly more valid CFEs compared to class-blind RL. Finally, the instance's class-based feature consistently ranks among the most influential predictors in RL's action-selection, as shown by the SHAP and LIME values, underscoring the significance of class-awareness in RL for CFE generation. The impact is heightened clarity, faster learning, improved validity, and more effective counterfactual generation across diverse datasets.

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Reference graph

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Reviewed July 31, 2026 · model on record in the stance chip above.

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AI and Ethics , volume=

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2026

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Interpretable multimodal emotion recognition using optimized transformer model with SHAP-based transparency , author=. The Journal of Supercomputing , volume=. 2025 , publisher=

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Explainable and Responsible Artificial Intelligence in Healthcare , pages=

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Computational and Structural Biotechnology Journal , year=

Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics , author=. Computational and Structural Biotechnology Journal , year=

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Neural Processing Letters , volume=

Recent emerging techniques in explainable artificial intelligence to enhance the interpretable and understanding of AI models for human , author=. Neural Processing Letters , volume=. 2025 , publisher=

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ACM Computing Surveys , volume=

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2025

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IEEE Access , year=

A Literature review on applications of explainable artificial intelligence (XAI) , author=. IEEE Access , year=

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Explainable AI in clinical decision support systems: a meta-analysis of methods, applications, and usability challenges , author=. Healthcare , volume=. 2025 , organization=

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