Missing data as a causal and probabilistic problem

Ilya Shpitser, Karthika Mohan, Judea Pearl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations


Causal inference is often phrased as a missing data problem - for every unit, only the response to observed treatment assignment is known, the response to other treatment assignments is not. In this paper, we extend the converse approach of [7] of representing missing data problems to causal models where only interventions on missingness indicators are allowed. We further use this representation to leverage techniques developed for the problem of identification of causal effects to give a general criterion for cases where a joint distribution containing missing variables can be recovered from data actually observed, given assumptions on missingness mechanisms. This criterion is significantly more general than the commonly used "missing at random" (MAR) criterion, and generalizes past work which also exploits a graphical representation of missingness. In fact, the relationship of our criterion to MAR is not unlike the relationship between the ID algorithm for identification of causal effects [22, 18], and conditional ignorability [13].

Original languageEnglish (US)
Title of host publicationUncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015
PublisherAUAI Press
Number of pages10
StatePublished - Jan 1 2015
Externally publishedYes
Event31st Conference on Uncertainty in Artificial Intelligence, UAI 2015 - Amsterdam, Netherlands
Duration: Jul 12 2015Jul 16 2015


Other31st Conference on Uncertainty in Artificial Intelligence, UAI 2015

ASJC Scopus subject areas

  • Artificial Intelligence

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