  Obtaining an explanation for an SQL query result can enrich the analysis
experience, reveal data errors, and provide deeper insight into the data.
Inference query explanation seeks to explain unexpected aggregate query results
on inference data; such queries are challenging to explain because an
explanation may need to be derived from the source, training, or inference data
in an ML pipeline. In this paper, we model an objective function as a black-box
function and propose BOExplain, a novel framework for explaining inference
queries using Bayesian optimization (BO). An explanation is a predicate
defining the input tuples that should be removed so that the query result of
interest is significantly affected. BO - a technique for finding the global
optimum of a black-box function - is used to find the best predicate. We
develop two new techniques (individual contribution encoding and warm start) to
handle categorical variables. We perform experiments showing that the
predicates found by BOExplain have a higher degree of explanation compared to
those found by the state-of-the-art query explanation engines. We also show
that BOExplain is effective at deriving explanations for inference queries from
source and training data on three real-world datasets.
