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.