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..
Learning Causal Relations from Subsampled Time Series with Two Time-Slices
Authors: Anpeng Wu, Haoxuan Li, Kun Kuang, Zhang Keli, Fei Wu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on both synthetic and real-world datasets demonstrate the superiority of our DHT-CIT algorithm. 5. Numerical Experiments |
| Researcher Affiliation | Collaboration | 1Department of Computer Science and Technology, Zhejiang University, Hangzhou, China 2Center for Data Science, Peking University, Beijing, China 3Huawei Noah s Ark Lab, Huawei, Shenzhen, China 4Shanghai Institute for Advanced Study, Zhejiang University, Shanghai, China 5Shanghai AI Laboratory, Shanghai, China. |
| Pseudocode | Yes | Algorithm 1 DHT-CIT: Descendant Hierarchical Topology with Conditional Independence Test |
| Open Source Code | Yes | The code of DHT-CIT is available at: https://github.com/anpwu/DHT-CIT. |
| Open Datasets | Yes | The PM-CMR (Wyatt et al., 2020) is a public time series dataset that is commonly used to study the impact of the particle (PM2.5, T) on the cardiovascular mortality rate (CMR, Y ) in 2132 counties in the US from 1990 to 2010. PM-CMR:https://pasteur.epa.gov/uploads/10.23719/1506014/SES_PM25_CMR_data.zip |
| Dataset Splits | No | The paper mentions generating synthetic data and using a sample size of 1000 for each replication, but it does not specify any training, validation, or test dataset splits or cross-validation methods. |
| Hardware Specification | Yes | Hardware used: Ubuntu 16.04.3 LTS operating system with 2 * Intel Xeon E5-2660 v3 @ 2.60GHz CPU (40 CPU cores, 10 cores per physical CPU, 2 threads per core), 256 GB of RAM, and 4 * Ge Force GTX TITAN X GPU with 12GB of VRAM. |
| Software Dependencies | Yes | Software used: Python 3.8 with cdt 0.6.0, ylearn 0.2.0, causal-learn 0.1.3, GPy 1.10.0, igraph 0.10.4, scikit-learn 1.2.2, networkx 2.8.5, pytorch 2.0.0. |
| Experiment Setup | Yes | In statistical hypothesis testing, α is typically set to 0.05 or 0.01. In this paper, we set the hyper-parameter α = 0.01 as the default. Algorithm 1 also lists input parameters: 'two significance threshold α = 0.01 and β = 0.001 for conditional independence test and pruning process'. |