Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data
Authors: Zijun Cui, Naiyu Yin, Yuru Wang, Qiang Ji
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show significant performance improvement in terms of both accuracy and efficiency over SOTA methods. We evaluate both the local and global constraint-based causal discovery performance on benchmark datasets. |
| Researcher Affiliation | Academia | 1Rensselaer Polytechnic Institute 2Northeast Normal University |
| Pseudocode | No | The paper describes methods mathematically and textually but does not contain any blocks explicitly labeled 'Pseudocode' or 'Algorithm', nor are there any structured steps formatted like code. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a repository for the methods developed in the paper. |
| Open Datasets | Yes | We employ six benchmark datasets1 that are widely used for causal discovery evaluation: CHILD, INSURANCE, ALARM, HAILFINDER, CHILD3 and CHILD5. 1https://www.bnlearn.com/bnrepository/. |
| Dataset Splits | No | The paper mentions evaluating performance on benchmark datasets with varying 'sample sizes' and repeating runs, but does not provide specific train/validation/test dataset splits, percentages, or absolute sample counts for partitioning data. |
| Hardware Specification | Yes | Experiments are performed on a laptop with a 8-Core Intel Core i9 processor with CPU only. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers required for reproducibility. |
| Experiment Setup | No | The paper describes general 'Experiment Settings' including datasets and evaluation metrics, but it does not provide specific hyperparameters (e.g., learning rate, batch size, epochs, optimizer settings) or other detailed system-level training configurations. |