Tractable Classification with Non-Ignorable Missing Data Using Generative Random Forests

• Julissa Villanueva Universidade de São Paulo
• Denis Mauá Universidade de São Paulo

DOI: https://doi.org/10.5753/kdmile.2022.227969

Resumo

Missing data is abundant in predictive tasks. Typical approaches assume that the missingness process is ignorable or non-informative and handle missing data either by marginalization or heuristically. Yet, data is often missing in a non-ignorable way, which introduce bias in prediction. In this paper, we develop a new method to perform tractable predictive inference under non-ignorable missing data using probabilistic circuits derived from Decision Tree Classifiers and a partially specified response model of missingness. We show empirically that our method delivers less biased (probabilistic) classifications than approaches that assume missing at random and are more determinate than similar existing overcautious approaches.

Palavras-chave: generative random forests, probabilistic circuits, non-ignorable missing data

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