  Causal inference from observational data is a subject of active research and
development in statistics and computer science. Many toolkits have been
developed for this purpose that depends on statistical software. However, these
toolkits do not scale to large datasets. In this paper we describe a suite of
techniques for expressing causal inference tasks from observational data in
SQL. This suite supports the state-of-the-art methods for causal inference and
run at scale within a database engine. In addition, we introduce several
optimization techniques that significantly speedup causal inference, both in
the online and offline setting. We evaluate the quality and performance of our
techniques by experiments of real datasets.
