Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Authors: Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and real-world data demonstrate the efficacy and robustness of our approach. |
| Researcher Affiliation | Academia | 1Guangdong University of Technology 2RIKEN 3Shiga University 4Peking University 5Okayama University 6Foshan University |
| Pseudocode | Yes | Algorithm 1 MD-Li NA Algorithm |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Yahoo stock indices dataset. and f MRI hippocampus dataset. ... [Poldrack et al., 2015]. |
| Dataset Splits | Yes | We used 10-fold cross validation to select parameter values. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions regularization parameters (λ1, λ2), a threshold (ϵ) for estimated effects, and uses 10-fold cross-validation for parameter selection, but does not provide specific numerical values for these hyperparameters or other system-level training settings. |