Ordering-Based Causal Discovery for Linear and Nonlinear Relations
Authors: Zhuopeng Xu, Yujie Li, Cheng Liu, Ning Gui
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our proposed solutions outperform state-of-the-art baselines on synthetic data with varying ratios of linear and nonlinear relations. The results obtained from real-world data also support the competitiveness of Ca PS. |
| Researcher Affiliation | Academia | Zhuopeng Xu Yujie Li Cheng Liu Ning Gui School of Computer Science and Engineering Central South University {xuzhuopeng, yujieli}@csu.edu.cn, {liuchengstudy, ninggui}@gmail.com |
| Pseudocode | Yes | Algorithm 1 Ordering and Computing parent score (page 5) and Algorithm 2 Post-processing (Appendix B, page 10). |
| Open Source Code | Yes | Code and datasets are available at https://github.com/E2real/Ca PS. |
| Open Datasets | Yes | Code and datasets are available at https://github.com/E2real/Ca PS. (Abstract) Synthetic data are created using the Erdös-Rényi (ER) [30] or Scale-Free (SF) models[31]... Real dataset contains a protein expression dataset Sachs [1] and a pseudoreal transport network dataset Syntern [32]. |
| Dataset Splits | No | The paper defines evaluation metrics but does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts) for the datasets used in the experiments. |
| Hardware Specification | Yes | All experiments were run on EPYC 7552*2 with 512G memory and NVIDIA RTX 4090 32GB. |
| Software Dependencies | No | The paper mentions methods and tools used (e.g., 'CAM pruning'), but it does not specify versions for software dependencies like programming languages or specific libraries. |
| Experiment Setup | Yes | The only hyperparameter of Ca PS was rigor λ, which we set to λ = 50 for all datasets to avoid any dataset-specific tuning. |