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..
Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering
Authors: Clément Yvernes, Emilie Devijver, Adèle Ribeiro, Marianne Clausel, Eric Gaussier
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we extend the C-DAG framework to support arbitrary variable clusterings by relaxing the partition admissibility constraint, thereby allowing cyclic C-DAG representations. We extend the notions of d-separation and causal calculus to this setting, significantly broadening the scope of causal reasoning across clusters and enabling the application of C-DAGs in previously intractable scenarios. Our calculus is both sound and atomically complete with respect to the do-calculus: all valid interventional queries at the cluster level can be derived using our rules, each corresponding to a primitive do-calculus step. All proofs are provided in the Technical Appendices. |
| Researcher Affiliation | Academia | Clément Yvernes Univ Grenoble Alpes, CNRS, Grenoble INP, LIG Emilie Devijver Univ Grenoble Alpes, CNRS, Grenoble INP, LIG Adèle H. Ribeiro University of Münster, Institute of Medical Informatics Marianne Clausel Université de Lorraine, CNRS, CRAN, 54000 Nancy Eric Gaussier Univ Grenoble Alpes, CNRS, Grenoble INP, LIG |
| Pseudocode | No | The paper focuses on theoretical contributions including definitions, theorems, and proofs related to causal calculus and graph theory. It describes procedures in natural language and mathematical notation but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to any code repositories. The NeurIPS checklist also indicates that 'The paper does not include experiments requiring code.' |
| Open Datasets | No | The paper is a theoretical work focusing on causal calculus. It discusses 'observational (non-experimental) data' in a general context but does not describe or use any specific datasets for empirical evaluation, nor does it provide access information for any dataset. |
| Dataset Splits | No | The paper is a theoretical study and does not involve experiments with datasets, therefore, no information regarding training, testing, or validation dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not present any empirical experiments that would require specific hardware. Consequently, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is a theoretical work and does not describe any experimental implementation. Therefore, no specific software dependencies or their versions are mentioned. |
| Experiment Setup | No | The paper is a theoretical study and does not include any experimental setup details, hyperparameters, or system-level training settings. |