Data attribution is the study of the relationship between data and ML predictions. In downstream applications, data attribution methods can help interpret and compare models; curate datasets; and assess learning algorithm stability.

This tutorial surveys the field of data attribution, with a focus on what we call “predictive data attribution.” We first motivate this notion within a broad, purpose-based taxonomy of data attribution. Next, we highlight how one can view predictive data attribution through the lens of a classic statistical problem that we call “weighted refitting." We discuss why classical methods for solving the weighted refitting problem struggle when directly applied to large-scale machine learning settings (and thus cannot directly solve problems in modern contexts). With these shortcomings in mind, we overview recent progress on performing predictive data attribution for modern ML models. Finally, we conclude by discussing applications---current and future---of data attribution.