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Missing Data from a Causal Perspective

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Book cover Advanced Methodologies for Bayesian Networks (AMBN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9505))

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Abstract

This paper applies graph based causal inference procedures for recovering information from missing data. We establish conditions that permit and prohibit recoverability. In the event of theoretical impediments to recoverability, we develop graph based procedures using auxiliary variables and external data to overcome such impediments. We demonstrate the perils of model-blind recovery procedures both in determining whether or not a query is recoverable and in choosing an estimation procedure when recoverability holds.

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Notes

  1. 1.

    The presence of a non-recoverable factor in a summand does not always imply the non-recoverability of the summand. See Example-3 in [18].

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Authors and Affiliations

  1. Department of Computer Science, University of California, Los Angeles, CA, 90095, USA

    Karthika Mohan & Judea Pearl

Authors
  1. Karthika Mohan
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  2. Judea Pearl
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Corresponding author

Correspondence to Karthika Mohan .

Editor information

Editors and Affiliations

  1. Osaka University, Osaka, Japan

    Joe Suzuki

  2. The University of Electro-Communications, Tokyo, Japan

    Maomi Ueno

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Mohan, K., Pearl, J. (2015). Missing Data from a Causal Perspective. In: Suzuki, J., Ueno, M. (eds) Advanced Methodologies for Bayesian Networks. AMBN 2015. Lecture Notes in Computer Science(), vol 9505. Springer, Cham. https://doi.org/10.1007/978-3-319-28379-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-28379-1_13

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-28379-1

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