Causal Structure Learning for Latent Intervened Non-stationary Data
Authors: Chenxi Liu, Kun Kuang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthetic and real-world datasets demonstrate that our method outperforms the baselines on causal structure learning for latent intervened non-stationary data. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Zhejiang University, Zhejiang, China. Correspondence to: Kun Kuang <kunkuang@zju.edu.cn>. |
| Pseudocode | Yes | Algorithm 1 Latent Intervention Learning |
| Open Source Code | Yes | Our code is available at LIN2023 on Git Hub. |
| Open Datasets | Yes | The dataset is provided by Copernicus Climate Change Service information (Hersbach et al., 2023). |
| Dataset Splits | No | The paper mentions 'hold-out data' for hyper-parameter selection and refers to 'test set' for evaluation, but does not provide specific percentages or sample counts for training, validation, and test splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions several software packages like 'causal-learn', 'lingam', 'tigramite', 'dynotears', and 'CPF SAEM' for baselines, and discusses neural networks for its method, but it does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Table 8. Hyper-parameter setting |