Monotone Missing Data: A Blessing and a Curse
Santtu Tikka, Juha Karvanen

TL;DR
This paper explores the dual nature of monotone missing data, showing it can both aid and hinder the identifiability of the full data distribution depending on the graphical structure.
Contribution
It provides a nuanced analysis of how monotonicity affects the identifiability of the full law in graphical models, highlighting cases where it helps or impedes.
Findings
Monotone missingness can enable identification of the full law in some cases.
Monotonicity can also prevent identification where nonmonotone cases succeed.
Proper treatment of monotone missing data is crucial for accurate analysis.
Abstract
Monotone missingness is commonly encountered in practice when a missing measurement compels another measurement to be missing. Because of the simpler missing data pattern, monotone missing data is often viewed as beneficial from the perspective of practical data analysis. However, in graphical missing data models, monotonicity has implications for the identifiability of the full law, i.e., the joint distribution of actual variables and response indicators. In the general nonmonotone case, the full law is known to be nonparametrically identifiable if and only if specific graphical structures are not present. We show that while monotonicity may enable the identification of the full law despite some of these structures, it also prevents the identification in certain cases that are identifiable without monotonicity. The results emphasize the importance of proper treatment of monotone…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGenetic and phenotypic traits in livestock · Statistical Methods and Inference
