mlsanityData to 14 Oct 2026

On Testability and Goodness of Fit Tests in Missing Data Models

arXiv 2203.00132 ↗First posted 28 Feb 2022stat.ME cs.LG stat.MLcited by 4

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Abstract (arXiv)

Significant progress has been made in developing identification and estimation techniques for missing data problems where modeling assumptions can be described via a directed acyclic graph. The validity of results using such techniques rely on the assumptions encoded by the graph holding true; however, verification of these assumptions has not received sufficient attention in prior work. In this paper, we provide new insights on the testable implications of three broad classes of missing data graphical models, and design goodness-of-fit tests for them. The classes of models explored are: sequential missing-at-random and missing-not-at-random models which can be used for modeling longitudinal studies with dropout/censoring, and a no self-censoring model which can be applied to cross-sectional studies and surveys.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 11 of its 37 references that are in the map. First posted on arXiv 28 Feb 2022.

Builds on

11 of 37 references.

References · 11

  1. 2018Graphical Models for Processing Missing Datacited by 58
  2. 2018Causal Discovery in the Presence of Missing Datacited by 47
  3. 2017Nested Markov Properties for Acyclic Directed Mixed Graphscited by 39
  4. 2020Full Law Identification In Graphical Models Of Missing Data: Completeness Resultscited by 26
  5. 2020Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variablescited by 23
  6. 2019Identification In Missing Data Models Represented By Directed Acyclic Graphscited by 19
  7. 2019Semiparametric Inference for Non-monotone Missing-Not-at-Random Data: the No Self-Censoring Modelcited by 12
  8. 2013Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphscited by 6
  9. 2016Itemwise conditionally independent nonresponse modeling for incomplete multivariate datacited by 6
  10. 2019Adjustment Criteria for Recovering Causal Effects from Missing Datacited by 2
  11. 2021Markov-Restricted Analysis of Randomized Trials with Non-Monotone Missing Binary Outcomescited by 2

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2023Sufficient Identification Conditions and Semiparametric Estimation under Missing Not at Random Mechanismscited by 4
  2. 2024MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Datacited by 1
  3. 2025Recursive Equations For Imputation Of Missing Not At Random Data With Sparse Pattern Supportcited by 1
  4. 2025Response to Discussions of "Causal and Counterfactual Views of Missing Data Models"cited by 0