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..
Cause-Effect Association between Event Pairs in Event Datasets
Authors: Debarun Bhattacharjya, Tian Gao, Nicholas Mattei, Dharmashankar Subramanian
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an experimental investigation with synthetic data and two real-world event datasets, where we evaluate and compare our proposed scores using assessments from human raters as ground truth. |
| Researcher Affiliation | Collaboration | 1 Research AI, IBM T. J. Watson Research Center 2 Department of Computer Science, Tulane University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code or provide a link to a code repository. |
| Open Datasets | Yes | We conduct an experimental investigation with synthetic data and two real-world event datasets: 1) a diabetes dataset [Frank and Asuncion, 2010; Acharya, 2014], and 2) the ICEWS political event dataset [O Brien, 2010] a relational (dyadic) event dataset where events are interactions between two actors. |
| Dataset Splits | Yes | We split the dataset into equal-sized training/test sets, determine a method s optimal hyper-parameter setting on the training set, and then compute the Hits@K on the test set using this hyper-parameter setting. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We ran experiments over a sweep of the hyperparameters: α {0, 0.5, 1, 2, 5}, λ {0, 0.25, 0.5, 0.75, 1} for NSTE, γ {0.001, 0.005, 0.01, 0.05, 0.1} for ECDE, g = {avg, max, min} for CIRM and window w = {7, 15, 30} days for all models, using support s = 10. [...] α {0, 1, 5}, λ {0, 0.5, 1} for NSTE, γ {0.001, 0.01, 0.1} for ECDE, g = {avg, max, min} for CIRM and window w = {0.1, 0.3, 0.5, 1} days for all models. |