ArticleProceedings of machine learning research2020
Full Law Identification in Graphical Models of Missing Data: Completeness Results.
Article in Proceedings of machine learning research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Investigations of sharp bounds for causal effects under selection bias.Statistical methods in medical research · 2025Article
- Pitfalls of imputing using incomplete auxiliary variables.American journal of epidemiology · 2025Article
- Mathur and Shpitser respond to "The evolution of selection bias in the recent epidemiologic literature-a selective overview".American journal of epidemiology · 2025Article
- Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions.Frontiers in immunology · 2025Review
- Analysis of Missingness Scenarios for Observational Health Data.Journal of personalized medicine · 2024Article
- Causal Inference With Outcome-Dependent Missingness And Self-Censoring.Proceedings of machine learning research · 2023Article
- Conditional generation of medical time series for extrapolation to underrepresented populations.PLOS digital health · 2022Article
- Leveraging Structured Biological Knowledge for Counterfactual Inference: A Case Study of Viral Pathogenesis.IEEE transactions on big data · 2021Article
- A Robust Functional EM Algorithm for Incomplete Panel Count Data.Advances in neural information processing systems · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Missing data has the potential to affect analyses conducted in all fields of scientific study including healthcare, economics, and the social sciences. Several approaches to unbiased inference in the presence of non-ignorable missingness rely on the specification of the target distribution and its missingness process as a probability distribution that factorizes with respect to a directed acyclic graph. In this paper, we address the longstanding question of the characterization of models that are identifiable within this class of missing data distributions. We provide the first completeness result in this field of study - necessary and sufficient graphical conditions under which, the full data distribution can be recovered from the observed data distribution. We then simultaneously address issues that may arise due to the presence of both missing data and unmeasured confounding, by extending these graphical conditions and proofs of completeness, to settings where some variables are not just missing, but completely unobserved.
Identifiers
33283197PMC7716645What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.