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 Discovery in Semi-Stationary Time Series
Authors: Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan Rossi, Murat Kocaoglu
NeurIPS 2023 | Venue PDF | 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. |