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Causal Discovery in the Presence of Missing Data

Tu, Ruibo (author)
KTH,Robotik, perception och lärande, RPL
Zhange, Cheng (author)
Microsoft Research, Cambridge, United Kingdom
Ackermann, Paul (author)
Karolinska Institute, Sweden
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Mohan, Karthika (author)
University of California, Berkeley, United States
Kjellström, Hedvig, 1973- (author)
KTH,Robotik, perception och lärande, RPL
Zhang, Kun (author)
Carnegie Mellon University, United States
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 (creator_code:org_t)
Microtome Publishing, 2020
2020
English.
In: 22nd international conference on artificial intelligence and statistics, vol 89. - : Microtome Publishing.
  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • Missing data are ubiquitous in many domains such as healthcare. When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be different from those in the complete data generated by the underlying causal process. Consequently, simply applying existing causal discovery methods to the observed data may lead to wrong conclusions. In this paper, we aim at developing a causal discovery method to recover the underlying causal structure from observed data that are missing under different mechanisms, including missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). With missingness mechanisms represented by missingness graphs (m-graphs), we analyze conditions under which additional correction is needed to derive conditional independence/dependence relations in the complete data. Based on our analysis, we propose Missing Value PC (MVPC), which extends the PC algorithm to incorporate additional corrections. Our proposed MVPC is shown in theory to give asymptotically correct results even on data that are MAR or MNAR. Experimental results on both synthetic data and real healthcare applications illustrate that the proposed algorithm is able to find correct causal relations even in the general case of MNAR.

Subject headings

NATURVETENSKAP  -- Matematik (hsv//swe)
NATURAL SCIENCES  -- Mathematics (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

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Tu, Ruibo
Zhange, Cheng
Ackermann, Paul
Mohan, Karthika
Kjellström, Hedv ...
Zhang, Kun
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NATURAL SCIENCES
NATURAL SCIENCES
and Mathematics
NATURAL SCIENCES
NATURAL SCIENCES
and Computer and Inf ...
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Royal Institute of Technology

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