  Identification and appropriate handling of inconsistencies in data at
deployment time is crucial to reliably use machine learning models. While
recent data-centric methods are able to identify such inconsistencies with
respect to the training set, they suffer from two key limitations: (1)
suboptimality in settings where features exhibit statistical independencies,
due to their usage of compressive representations and (2) lack of localization
to pin-point why a sample might be flagged as inconsistent, which is important
to guide future data collection. We solve these two fundamental limitations
using directed acyclic graphs (DAGs) to encode the training set's features
probability distribution and independencies as a structure. Our method, called
DAGnosis, leverages these structural interactions to bring valuable and
insightful data-centric conclusions. DAGnosis unlocks the localization of the
causes of inconsistencies on a DAG, an aspect overlooked by previous
approaches. Moreover, we show empirically that leveraging these interactions
(1) leads to more accurate conclusions in detecting inconsistencies, as well as
(2) provides more detailed insights into why some samples are flagged.
