Causal What-If and How-To Analysis Using HypeR

Fangzhu Shen*, Kayvon Heravi, Oscar Gomez, Sainyam Galhotra, Amir Gilad, Sudeepa Roy, Babak Salimi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9798350322279
StatePublished - 2023
Externally publishedYes
Event39th IEEE International Conference on Data Engineering, ICDE 2023 - Anaheim, United States
Duration: 3 Apr 20237 Apr 2023

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference39th IEEE International Conference on Data Engineering, ICDE 2023
Country/TerritoryUnited States


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