Venue: UAI 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate to high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations. 
review_content: Q1 Summary and contributions: The paper proposes an approach for attributing predictive
uncertainties in machine learning classifiers. The proposed approach
that combines path integrals, counterfactual explanations and
generative models.
Q3 Main strengths: The paper covers the topic of predictive uncertainties. The paper is 
well-written, and contributes a tool to a concrete problem.
Q4 Main weakness: The work is heuristic in nature, and so lacking on the theoretical side.
Q5 Detailed comments to the authors: The paper reads well.

Some results in Table 1 seem to be of extremely poor quality,
e.g. LIME and some of the XRAI variants. It would be helpful to
explain clearly why that is the case.
Q2(4) Quality of experiments (Optional): 3: Good: The experimental evaluation is adequate, and the results convincingly support the main claims.
Q7 Justification for your score: The paper addresses of topic of interest, proposes a solution, and it
is well written.
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper with, with novel ideas, excellent impact on at least one area of AI or high to excellent impact on multiple areas of AI, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations. 
review_content: Q1 Summary and contributions: This paper proposes a novel framework to attribute predictive uncertainties to the input features of a deep-learning model, which combines path integrals, counterfactual explanations, and generative models.
Q3 Main strengths: Solid work, with convincing theory and compelling empirical validation
Q4 Main weakness: Originality of the work consist essentially of combining previously existing ideas.
Q5 Detailed comments to the authors: I'm not particularly knowledgeable about image processing, so I don't have detailed comments to offer. I did not take the appendices into account, but even without them the theory and empirical validation look compelling enough. Nice contribution!
Q2(4) Quality of experiments (Optional): 3: Good: The experimental evaluation is adequate, and the results convincingly support the main claims.
Q7 Justification for your score: This is solid work, fully relevant to the main topic of this conference.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate to high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations. 
review_content: Q1 Summary and contributions: This work investigates an important research question. The proposed approach is well formulated and the experimental results demonstrate the effectiveness of the proposed approach. 
Q3 Main strengths: 1.	This work investigates an important research problem. 
2.	The proposed approach is the combination of several existing ideas and is verified by the experiments. 

Q4 Main weakness: Although this paper performs a few experiments, the scale of the dataset is not large enough. Moreover, the dataset is comparatively simple and more challenging dataset is desired to verify the model performance.  
Q5 Detailed comments to the authors: This paper is well formulated and is clearly written. Experiments partially verify the effectiveness of the proposed approach. However, a more challenging dataset is desired to valuate the model performance. 
Q2(4) Quality of experiments (Optional): 2: Fair: The experimental evaluation is weak: important baselines are missing, or the results do not adequately support the main claims.
Q7 Justification for your score: This paper is well formulated and is clearly written. Experiments partially verify the effectiveness of the proposed approach.
review_title: null

Meta-review: Meta Review: The manuscript introduces a novel framework for the attribution of predictive uncertainties in classification models, which combines path methods, counterfactual explanations (not in a causal sense) and generative models.

While the work does not contain a theoretical analysis of the proposed method, the reviewers have agreed that the proposed methodology is well motivated and shows compelling empirical results. 
Decision: Accept (Poster)