The talk will discuss recent advances in the field of missing data, including: 1)  the graphical representation called "Missingness Graph" that portrays the causal mechanisms responsible for missingness, 2) the notion of recoverability, i.e. deciding whether there exists a consistent estimator for a given query, 3) graphical conditions (necessary and sufficient) for recovering joint and conditional distributions and algorithms for detecting these conditions in the missingness graph, 4) the question of testability i.e. whether an assumed model can be subjected to statistical tests, considering the missingness in the data and 5) the indispensability of causal assumptions for large sets of missing data problems.

Karthika Mohan is a PhD Candidate in the department of Computer Science at UCLA majoring in Artificial Intelligence. She is advised by Prof Judea Pearl. Her research interests span areas in Artificial Intelligence and Statistics that intersect with Probabilistic Graphical Models, Causal Inference and Missing Data

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