  Understanding the behavior of learned classifiers is an important task, and
various black-box explanations, logical reasoning approaches, and
model-specific methods have been proposed. In this paper, we introduce
probabilistic sufficient explanations, which formulate explaining an instance
of classification as choosing the "simplest" subset of features such that only
observing those features is "sufficient" to explain the classification. That
is, sufficient to give us strong probabilistic guarantees that the model will
behave similarly when all features are observed under the data distribution. In
addition, we leverage tractable probabilistic reasoning tools such as
probabilistic circuits and expected predictions to design a scalable algorithm
for finding the desired explanations while keeping the guarantees intact. Our
experiments demonstrate the effectiveness of our algorithm in finding
sufficient explanations, and showcase its advantages compared to Anchors and
logical explanations.
