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..
Telling cause from effect in deterministic linear dynamical systems
Authors: Naji Shajarisales, Dominik Janzing, Bernhard Schoelkopf, Michel Besserve
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show encouraging results on synthetic as well as real-world data. |
| Researcher Affiliation | Academia | 1 MPI for Intelligent Systems, Tuebingen, Germany 2 MPI for Biological Cybernetics , Tuebingen, Germany |
| Pseudocode | Yes | Algorithm 1 SIC Inference |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its own source code for the methodology, nor does it provide a direct link to a code repository for its implementation. The mentioned links refer to datasets or third-party libraries. |
| Open Datasets | Yes | To do a comparison with Granger causality, we applied our framework to recordings from those regions using a publicly available dataset1 (Mizuseki et al., 2009; 2006). 1http://crcns.org/data-sets/hc |
| Dataset Splits | No | The paper describes generating synthetic data and processing real-world time series by dividing them into intervals, but it does not specify explicit training, validation, and test dataset splits with proportions or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'statsmodel Python library' but does not provide specific version numbers for Python, statsmodels, or any other ancillary software components. |
| Experiment Setup | Yes | We simulated sequences of length 1000. The PSD of X and Y were estimated using Welch s method (Welch, 1967). (Section 4.1); We divided the duration of ten minutes into 300 intervals of two seconds (N = 2504) to reduce the effect of nonstationarity in data analysis, and performed SIC causal inference on each interval for each electrode pair. (Section 4.2.3) |