Causal Discovery in Semi-Stationary Time Series
Authors: Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan Rossi, Murat Kocaoglu
NeurIPS 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the algorithm with extensive experiments on continuous and discrete simulated data. We also apply our algorithm to a real-world climate dataset. |
| Researcher Affiliation | Collaboration | Shanyun Gao Purdue University EMAIL Raghavendra Addanki Adobe Research EMAIL Tong Yu Adobe Research EMAIL Ryan A. Rossi Adobe Research EMAIL Murat Kocaoglu Purdue University EMAIL |
| Pseudocode | Yes | Algorithm 1 PCMCIΩ, Algorithm A1 PCqmax, Algorithm A2 MCI, Algorithm B1 PCMCIΩ |
| Open Source Code | Yes | The Python code is provided at https://github.com/Causal ML-Lab/PCMCI-Omega. |
| Open Datasets | No | The paper mentions using "continuous and discrete simulated data" and a "real-world climate dataset" from 1948-2022, but it does not provide concrete access information (link, DOI, specific citation with authors/year) for the climate dataset, nor does it make the simulated data publicly available. |
| Dataset Splits | No | The paper describes how synthetic data is generated but does not specify explicit training, validation, or test splits. It states that performance statistics are averaged over 100 trials. |
| Hardware Specification | Yes | All experiments, including those detailed in the main paper, are conducted on a single node with one core, utilizing 512 GB of memory in the Gilbreth cluster at Purdue University. |
| Software Dependencies | No | The paper mentions that "The Python code is provided at https://github.com/Causal ML-Lab/PCMCI-Omega." implying Python is used, but it does not provide specific version numbers for Python or any other libraries/packages. |
| Experiment Setup | Yes | Set τub = 15, ωub = 15 for all variables. |