@inproceedings{94f75650c4e74ff7a3f08f0c2961e99d,
title = "Causal What-If and How-To Analysis Using HypeR",
abstract = "What-if and How-to queries are fundamental data analysis questions that provide insights about the effects of a hypothetical update without actually making changes to the database. Traditional systems assume independence across differ¬ent tuples and non-updated attributes of the database. However, different attributes and tuples are generally dependent in real-world scenarios. We propose to demonstrate HypeR, a novel system to efficiently answer what-if and how-to queries while capturing causal dependencies among different attributes and tuples in the database. To compute the results, HypeR leverages a suite of optimizations along with techniques from causal inference to effectively estimate the answers. HypeR allows users to formulate complex hypothetical queries by using a novel SQL-like syntax and presents the output as interactive visualizations that can be explored and analyzed with ease.",
author = "Fangzhu Shen and Kayvon Heravi and Oscar Gomez and Sainyam Galhotra and Amir Gilad and Sudeepa Roy and Babak Salimi",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 39th IEEE International Conference on Data Engineering, ICDE 2023 ; Conference date: 03-04-2023 Through 07-04-2023",
year = "2023",
doi = "10.1109/ICDE55515.2023.00293",
language = "الإنجليزيّة",
series = "Proceedings - International Conference on Data Engineering",
publisher = "IEEE Computer Society",
pages = "3663--3666",
booktitle = "Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023",
address = "الولايات المتّحدة",
}