Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Authors: Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao
IJCAI 2021 | Venue PDF | 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. |