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Learning from data with structured missingness


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.

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Fig. 1: The data missingness life cycle.
Fig. 2: Examples of SM.


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This work was sponsored by the Turing-Roche Strategic Partnership. We thank C. Matus for her talents in figure illustrations and design and V. Hellon for her expert community management.

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Correspondence to Robin Mitra, Sarah F. McGough, Chris Harbron or Ben D. MacArthur.

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Mitra, R., McGough, S.F., Chakraborti, T. et al. Learning from data with structured missingness. Nat Mach Intell 5, 13–23 (2023).

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