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 Structure Learning for Latent Intervened Non-stationary Data
Authors: Chenxi Liu, Kun Kuang
ICML 2023 | Venue PDF | 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 <EMAIL>. |
| 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 |