The Structurally Complex with Additive Parent Causality (SCARY) Dataset

Autor:	Chen, Jarry, Fayek, Haytham M.
Rok vydání:	2023
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Causal datasets play a critical role in advancing the field of causality. However, existing datasets often lack the complexity of real-world issues such as selection bias, unfaithful data, and confounding. To address this gap, we propose a new synthetic causal dataset, the Structurally Complex with Additive paRent causalitY (SCARY) dataset, which includes the following features. The dataset comprises 40 scenarios, each generated with three different seeds, allowing researchers to leverage relevant subsets of the dataset. Additionally, we use two different data generation mechanisms for generating the causal relationship between parents and child nodes, including linear and mixed causal mechanisms with multiple sub-types. Our dataset generator is inspired by the Causal Discovery Toolbox and generates only additive models. The dataset has a Varsortability of 0.5. Our SCARY dataset provides a valuable resource for researchers to explore causal discovery under more realistic scenarios. The dataset is available at https://github.com/JayJayc/SCARY. Comment: 5 pages, 5 figures, accepted to CLeaR (Causal Learning and Reasoning) 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.14109 Zobrazit plný text záznamu View this record from Arxiv