Explore a comprehensive oral presentation from the Uncertainty in Artificial Intelligence (UAI) 2023 conference focusing on testability and goodness-of-fit tests in missing data models. Delve into the research conducted by Razieh Nabi and Rohit Bhattacharya, which addresses the crucial issue of verifying assumptions in graphical models for missing data problems. Learn about new insights on testable implications for three broad classes of missing data graphical models: sequential missing-at-random, missing-not-at-random, and no self-censoring models. Discover how these models apply to longitudinal studies with dropout/censoring and cross-sectional studies. Gain valuable knowledge on the development of goodness-of-fit tests for these models, enhancing the reliability of results in missing data analysis. Access the presentation slides for a visual aid to complement the 26-minute talk, providing a deeper understanding of this significant contribution to the field of artificial intelligence and data analysis. Read more