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 Inference in Time Series via Supervised Learning
Authors: Yoichi Chikahara, Akinori Fujino
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, our method sufficiently outperformed the model-based Granger causality methods and the supervised learning method for i.i.d. data by using the same feature representation and the same classifier. |
| Researcher Affiliation | Industry | Yoichi Chikahara and Akinori Fujino NTT Communication Science Laboratories, Kyoto 619-0237, Japan EMAIL, EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | No | The paper provides links to the code for existing methods (RCC, GCVAR, GCGAM, GCKER, TE) that are used for comparison, but not for the authors' proposed method (SIGC). |
| Open Datasets | Yes | The first test dataset was composed of five pairs of bivariate time series downloaded from the Cause-Effect Pairs database [Jakob, ], whose true causal relationships are reported in [Jakob, ] as X Y for three pairs and as X Y for the others. For instance, the River Runoff is a bivariate time series concerning average precipitation X and average river runoff Y , and the true causal relationship is regarded as X Y ." and "[Jakob, ] Zscheischler Jakob. Database with cause-effect pairs. https://webdav.tuebingen.mpg.de/cause-effect/.\" and \"We used the Saccharomyces cerevisiae (yeast) cell cycle gene expression dataset collected by [Spellman et al., 1998]. |
| Dataset Splits | Yes | The number of trees is selected from {100, 200, 500, 1000, 2000} via 5-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using a random forest classifier and Gaussian kernels, but does not provide specific version numbers for these software components or any other libraries used. |
| Experiment Setup | Yes | The number of trees is selected from {100, 200, 500, 1000, 2000} via 5-fold cross validation." and "For our method, we selected W = 12.\" and \"we set the number of features m = 100\" and \"Finally, we scaled each time series with mean 0 and variance 1. |