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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Conditions and Assumptions for Constraint-based Causal Structure Learning
Authors: Kayvan Sadeghi, Terry Soo
JMLR 2022 | Venue PDF | LLM Run Details | Input Tokens: 29,121 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,134 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We stress that in this paper, we do not provide specific algorithms that could be implemented, but concentrate on providing conditions that structure-learning algorithms should satisfy. |
| Researcher Affiliation | Academia | Kayvan Sadeghi EMAIL Department of Statistical Science University College London London, United Kingdom Terry Soo EMAIL Department of Statistical Science University College London London, United Kingdom |
| Pseudocode | No | We stress that in this paper, we do not provide specific algorithms that could be implemented, but concentrate on providing conditions that structure-learning algorithms should satisfy. |
| Open Source Code | No | We stress that in this paper, we do not provide specific algorithms that could be implemented, but concentrate on providing conditions that structure-learning algorithms should satisfy. |
| Open Datasets | No | The paper presents theoretical work on causal structure learning and does not involve empirical experiments using datasets. |
| Dataset Splits | No | The paper presents theoretical work and does not involve empirical experiments, therefore, there is no mention of dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical conditions and assumptions for causal structure learning and does not describe any experimental hardware. |
| Software Dependencies | No | The paper is theoretical and does not include any experimental implementation or specific software dependencies with version numbers. |
| Experiment Setup | No | The paper presents theoretical work and does not detail any experimental setup, hyperparameters, or training configurations. |