Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Ordering-Based Causal Discovery for Linear and Nonlinear Relations
Authors: Zhuopeng Xu, Yujie Li, Cheng Liu, Ning Gui
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 31,978 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,486 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL, EMAIL |
| 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. |