Conditions and Assumptions for Constraint-based Causal Structure Learning
Authors: Kayvan Sadeghi, Terry Soo
JMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |