Observational data is available today in multi-relational form, often extracted from various sources, and stored in multiple flat and interrelated tables. Standard statistical methods for conducting causal inference on observational data assume a very simple data model: a single table with independent units. This research has the potential to significantly impact application domains where differentiating causality from correlation is essential, e.g., education policy and cancer genomics. The HUME project develops techniques for efficient causal analysis using a declarative approach, over complex views, and over large datasets that are integrated from disparate data sources. HUME uses a SQL-like language and is integrated with a relational database system.

The project develops techniques for defining arbitrarily complex units, treatments, outcomes, and covariates, by combining joins, data mapping, and aggregates across multiple tables, and uses a causal network to choose a good set of covariates for causal inference. The first part of the project develops scalable techniques for sub-classification and matching for large data sets obtained by declaratively integrating multiple data sources. The second part of the project develops scalable methods for discovering causal relationships among the attributes in the views by constraint-based, search-based, and hybrid discovery processes. Finally, the third part of the project investigates interferences among units arising from the complex views by designing normal forms and automatic inference of underlying assumptions exploiting techniques from database theory.

Agency
National Science Foundation (NSF)
Institute
Division of Information and Intelligent Systems (IIS)
Type
Standard Grant (Standard)
Application #
1703281
Program Officer
Maria Zemankova
Project Start
Project End
Budget Start
2017-09-01
Budget End
2020-08-31
Support Year
Fiscal Year
2017
Total Cost
$408,000
Indirect Cost
Name
University of Washington
Department
Type
DUNS #
City
Seattle
State
WA
Country
United States
Zip Code
98195