Cause-Effect Association between Event Pairs in Event Datasets
Authors: Debarun Bhattacharjya, Tian Gao, Nicholas Mattei, Dharmashankar Subramanian
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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. |