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

TL;DR

This paper discusses the application of causal and counterfactual frameworks to missing data problems, emphasizing the use of m-DAGs for identification in MNAR models and exploring related causal inference tools.

Contribution

It highlights the utility of causal/counterfactual perspectives and m-DAGs in identifying MNAR models without relying on parametric assumptions.

Findings

Use of m-DAGs aids in identifying complex MNAR models.

Causal inference tools can enhance missing data analysis.

Discussion of estimation approaches in MNAR models.

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.