  When analyzing large datasets, analysts are often interested in the
explanations for surprising or unexpected results produced by their queries. In
this work, we focus on aggregate SQL queries that expose correlations in the
data. A major challenge that hinders the interpretation of such queries is
confounding bias, which can lead to an unexpected correlation. We generate
explanations in terms of a set of confounding variables that explain the
unexpected correlation observed in a query. We propose to mine candidate
confounding variables from external sources since, in many real-life scenarios,
the explanations are not solely contained in the input data. We present an
efficient algorithm that finds the optimal subset of attributes (mined from
external sources and the input dataset) that explain the unexpected
correlation. This algorithm is embodied in a system called MESA. We demonstrate
experimentally over multiple real-life datasets and through a user study that
our approach generates insightful explanations, outperforming existing methods
that search for explanations only in the input data. We further demonstrate the
robustness of our system to missing data and the ability of MESA to handle
input datasets containing millions of tuples and an extensive search space of
candidate confounding attributes.
