Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Cyclic Counterfactuals under Shift–Scale Interventions
Authors: Saptarshi Saha, Dhruv Rathore, Utpal Garain
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The research work is theoretical in nature. |
| Researcher Affiliation | Academia | Saptarshi Saha Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata, West Bengal 700108, India EMAIL Dhruv Vansraj Rathore Indian Statistical Institute Kolkata, West Bengal 700108, India EMAIL Utpal Garain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata, West Bengal 700108, India EMAIL |
| Pseudocode | No | The paper describes theoretical concepts, theorems, and proofs but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | Justification: The research work is theoretical in nature. |
| Open Datasets | No | Justification: The research work is theoretical in nature. |
| Dataset Splits | No | The paper is theoretical and does not use any datasets that would require splits. |
| Hardware Specification | No | Justification: The research work is theoretical in nature. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not present experimental results or an experimental setup. |