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 [1].
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. |