  Missing data are an unavoidable complication in many machine learning tasks.
When data are `missing at random' there exist a range of tools and techniques
to deal with the issue. However, as machine learning studies become more
ambitious, and seek to learn from ever-larger volumes of heterogeneous data, an
increasingly encountered problem arises in which missing values exhibit an
association or structure, either explicitly or implicitly. Such `structured
missingness' raises a range of challenges that have not yet been systematically
addressed, and presents a fundamental hindrance to machine learning at scale.
Here, we outline the current literature and propose a set of grand challenges
in learning from data with structured missingness.
