Telling cause from effect in deterministic linear dynamical systems
Authors: Naji Shajarisales, Dominik Janzing, Bernhard Schoelkopf, Michel Besserve
ICML 2015 | Conference PDF | Archive PDF | Plain Text | 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) |