2016/11/15 by Jin Tian, Tian, Jin
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Management and Algorithms #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.1611.04709
openalex publication_date 2016/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A probabilistic query may not be estimable from observed data corrupted by missing values if the data are not missing at random (MAR). It is therefore of theoretical interest and practical importance to determine in principle whether a probabilistic query is estimable from missing data or not when the data are not MAR. We present an algorithm that systematically determines whether the joint probability is estimable from observed data with missing values, assuming that the data-generation model is represented as a Bayesian network containing unobserved latent variables that not only encodes the dependencies among the variables but also explicitly portrays the mechanisms responsible for the missingness process. The result significantly advances the existing work.