Graphical Models based Solutions for Missing Data Problems

Jul 17, 2020

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“Missingness Graphs” (m-graphs) are causal graphical models used for processing missing data. They portray the causal mechanisms responsible for missingness and thus encode knowledge about the underlying process that generates data. Using m-graphs, we develop methods to determine if there exists a consistent estimator for a given quantity of interest such as joint distributions, conditional distributions and causal effects. Our methods apply to all types of missing data including the notorious and relatively unexplored NMAR (Not Missing At Random) category. We further address the question of testability i.e. if and how an assumed model can be subjected to statistical tests, considering the missingness in the data. Viewing the missing data problem from a causal perspective has ushered in several surprises such as recoverability when variables are causes of their own missingness, testability of MAR models and the indispensability of causal assumptions for handling missing data problems.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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