ZaliQL: causal inference from observational data at scale

Causal inference from observational data is a subject of active research and development in statistics and computer science. Many statistical software packages have been developed for this purpose. However, these toolkits do not scale to large datasets. We propose and demonstrate ZaliQL: a SQL-based...

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Bibliographic Details
Published in:Proceedings of the VLDB Endowment 2017-08, Vol.10 (12), p.1957-1960
Main Authors: Salimi, Babak, Cole, Corey, Ports, Dan R. K., Suciu, Dan
Format: Article
Language:English
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Summary:Causal inference from observational data is a subject of active research and development in statistics and computer science. Many statistical software packages have been developed for this purpose. However, these toolkits do not scale to large datasets. We propose and demonstrate ZaliQL: a SQL-based framework for drawing causal inference from observational data. ZaliQL supports the state-of-the-art methods for causal inference and runs at scale within PostgreSQL database system. In addition, we built a visual interface to wrap around ZaliQL. In our demonstration, we will use this GUI to show a live investigation of the causal effect of different weather conditions on flight delays.
ISSN:2150-8097
2150-8097
DOI:10.14778/3137765.3137818