• Corpus ID: 215737167

Full Law Identification In Graphical Models Of Missing Data: Completeness Results

@article{Nabi2020FullLI,
  title={Full Law Identification In Graphical Models Of Missing Data: Completeness Results},
  author={Razieh Nabi and Rohit Bhattacharya and Ilya Shpitser},
  journal={Proceedings of machine learning research},
  year={2020},
  volume={119},
  pages={
          7153-7163
        }
}
Missing data has the potential to affect analyses conducted in all fields of scientific study including healthcare, economics, and the social sciences. Several approaches to unbiased inference in the presence of non-ignorable missingness rely on the specification of the target distribution and its missingness process as a probability distribution that factorizes with respect to a directed acyclic graph. In this paper, we address the longstanding question of the characterization of models that… 

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