Effective Causal Discovery under Identifiable Heteroscedastic Noise Model
Authors: Naiyu Yin, Tian Gao, Yue Yu, Qiang Ji
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show significant empirical gains of the proposed approaches over state-of-the-art methods on both synthetic data and real data. |
| Researcher Affiliation | Collaboration | Naiyu Yin1, Tian Gao2, Yue Yu3, Qiang Ji1 1Rensselaer Polytechnic Institute, Troy, NY. 2IBM Research, Yorktown Heights, NY. 3Lehigh University, Bethlehem, PA. |
| Pseudocode | No | We outline the full procedure (Algorithm 1), Phase-I procedure (Algorithm 2), Phase-II procedure (Algorithm 3) in supplementary Section E. |
| Open Source Code | No | Please refer to the ar Xiv version of this paper for supplementary materials. https://arxiv.org/abs/2312.12844 |
| Open Datasets | No | We apply our method and baselines to two real datasets: Sachs and cause-effect pairs. |
| Dataset Splits | No | No specific information about training/validation/test dataset splits is provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models or memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper describes the model architecture (2-layer MLPs, activation functions) and optimization method (ALM, k iterations of two phases) but does not provide specific numerical hyperparameter values (e.g., learning rate, batch size, number of epochs). |