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Graphical Models for Inference with Missing Data
Karthika Mohan · Judea Pearl · Jin Tian

Fri Dec 06 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor #None
We address the problem of deciding whether there exists a consistent estimator of a given relation Q, when data are missing not at random. We employ a formal representation called `Missingness Graphs' to explicitly portray the causal mechanisms responsible for missingness and to encode dependencies between these mechanisms and the variables being measured. Using this representation, we define the notion of \textit{recoverability} which ensures that, for a given missingness-graph $G$ and a given query $Q$ an algorithm exists such that in the limit of large samples, it produces an estimate of $Q$ \textit{as if} no data were missing. We further present conditions that the graph should satisfy in order for recoverability to hold and devise algorithms to detect the presence of these conditions.

Author Information

Karthika Mohan (UC Berkeley)
Judea Pearl (UCLA)

Judea Pearl is a professor of computer science and statistics at UCLA. He is a graduate of the Technion, Israel, and has joined the faculty of UCLA in 1970, where he conducts research in artificial intelligence, causal inference and philosophy of science. Pearl has authored three books: Heuristics (1984), Probabilistic Reasoning (1988), and Causality (2000;2009), the latter won the Lakatos Prize from the London School of Economics. He is a member of the National Academy of Engineering, the American Academy of Arts and Sciences, and a Fellow of the IEEE, AAAI and the Cognitive Science Society. Pearl received the 2008 Benjamin Franklin Medal from the Franklin Institute and the 2011 Rumelhart Prize from the Cognitive Science Society. In 2012, he received the Technion's Harvey Prize and the ACM Alan M. Turing Award.

Jin Tian (Iowa State University)

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