Discussion of "Causal and counterfactual views of missing data models" by Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser, & James M. Robins

2025/06/16 by Alex W. Levis, Edward H. Kennedy, Levis, Alex W. +1
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Statistical Methods and Bayesian Inference #Bayesian Modeling and Causal Inference

paper · pdf · doi:10.48550/arxiv.2506.13025

Abstract

We congratulate Nabi et al. (2022) on their impressive and insightful paper, which illustrates the benefits of using causal/counterfactual perspectives and tools in missing data problems. This paper represents an important approach to missing-not-at-random (MNAR) problems, exploiting nonparametric independence restrictions for identification, as opposed to parametric/semiparametric models, or resorting to sensitivity analysis. Crucially, the authors represent these restrictions with missing data directed acyclic graphs (m-DAGs), which can be useful to determine identification in complex and interesting MNAR models. In this discussion we consider: (i) how/whether other tools from causal inference could be useful in missing data problems, (ii) problems that combine both missing data and causal inference together, and (iii) some work on estimation in one of the authors' example MNAR models.

Citations

• Single Proxy Control
• Covariate-assisted bounds on causal effects with instrumental variables
• Covariate-assisted bounds on causal effects with instrumental variables
• Causal and Counterfactual Views of Missing Data Models
• Semiparametric doubly robust targeted double machine learning: a review
• Nonparametric inference about mean functionals of nonignorable nonresponse data without identifying the joint distribution
• Semiparametric Proximal Causal Inference
• Multivariate Counterfactual Systems And Causal Graphical Models
• Efficient nonparametric causal inference with missing exposure information
• Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables
• Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder
• On Varieties of Doubly Robust Estimators Under Missingness Not at Random With a Shadow Variable
• Sample Selection Bias as a Specification Error

Related

• Causal and Counterfactual Views of Missing Data Models
• Assumptions and analysis planning in studies with missing data in multiple variables: moving beyond the MCAR/MAR/MNAR classification
• Canonical Causal Diagrams to Guide the Treatment of Missing Data in Epidemiologic Studies
• Graphical Models for Processing Missing Data
• Using causal diagrams to guide analysis in missing data problems