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..
Fast Estimation of Causal Interactions using Wold Processes
Authors: Flavio Figueiredo, Guilherme Resende Borges, Pedro O.S. Vaz de Melo, Renato Assunção
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach, called GRANGER-BUSCA, is validated on nine datasets. This is an advance in relation to most prior efforts which focus mostly on subsets of the Memetracker data. Regarding accuracy, GRANGER-BUSCA is three times more accurate (in Precision@10) than the state of the art for the commonly explored subsets Memetracker. and 5 Results and Experiments and We evaluate GRANGER-BUSCA and the aforementioned three baselines on 9 different datasets |
| Researcher Affiliation | Academia | Flavio Figueiredo Guilherme Borges Pedro O. S. Vaz de Melo Renato Assunc ao Universidade Federal de Minas Gerais (UFMG) EMAIL |
| Pseudocode | Yes | In Algorithm 1 we show how Ogata s Modified Thinning algorithm [38] is adapted for GRANGERBUSCA. |
| Open Source Code | Yes | Reproducibility: http://github.com/flaviovdf/granger-busca |
| Open Datasets | Yes | Datasets: We evaluate GRANGER-BUSCA and the aforementioned three baselines on 9 different datasets, all of which were gathered from the Snap Network Repository5. ... 5https://snap.stanford.edu/data/ and Memetracker data [29] |
| Dataset Splits | No | The paper does not explicitly provide details on train/validation/test dataset splits, such as percentages, sample counts, or specific predefined splits for reproducibility. |
| Hardware Specification | Yes | Experiments were performed on a dedicated Azure Standard DS15 v2 instance with 20 Intel Xeon CPU E5-2673 v3 cores and 140GB of memory. |
| Software Dependencies | Yes | The code for each method is publicly available via the library tick4. ... 4https://github.com/X-Data Initiative/tick. Results produced with version 0.4.0.0. |
| Experiment Setup | Yes | Training is performed until convergence or until 300 iterations is reached. Our MCMC sampler executes for 300 iterations with αp = 1/K and β = 1. and For Hawkes-Granger, we fit the model with M = 10 basis functions as suggested in [1]. |