# Response to Discussions of "Causal and Counterfactual Views of Missing Data Models"

arXiv: https://arxiv.org/abs/2510.14694 · HTML: https://mlsanity.com/paper/2510.14694/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Builds on (most cited first, 19 of 19)

- [Graphical Models for Processing Missing Data](https://mlsanity.com/paper/1801.03583/) (2018)
- [An Automated Approach to Causal Inference in Discrete Settings](https://mlsanity.com/paper/2109.13471/) (2021)
- [Full Law Identification In Graphical Models Of Missing Data: Completeness Results](https://mlsanity.com/paper/2004.04872/) (2020)
- [Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variables](https://mlsanity.com/paper/1302.4977/) (2013)
- [Identification In Missing Data Models Represented By Directed Acyclic Graphs](https://mlsanity.com/paper/1907.00241/) (2019)
- [Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach](https://mlsanity.com/paper/1902.01073/) (2019)
- [Semiparametric Inference for Non-monotone Missing-Not-at-Random Data: the No Self-Censoring Model](https://mlsanity.com/paper/1909.01848/) (2019)
- [Semiparametric Estimation with Data Missing Not at Random Using an Instrumental Variable](https://mlsanity.com/paper/1607.03197/) (2016)
- [Ananke: A Python Package For Causal Inference Using Graphical Models](https://mlsanity.com/paper/2301.11477/) (2023)
- [Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search](https://mlsanity.com/paper/2308.07346/) (2023)
- [Post-selection inference for causal effects after causal discovery](https://mlsanity.com/paper/2405.06763/) (2024)
- [Confidence Sets for Causal Orderings](https://mlsanity.com/paper/2305.14506/) (2023)
- [Causal Inference With Outcome-Dependent Missingness And Self-Censoring](https://mlsanity.com/paper/2306.05511/) (2023)
- [On Testability and Goodness of Fit Tests in Missing Data Models](https://mlsanity.com/paper/2203.00132/) (2022)
- [A self-censoring model for multivariate nonignorable nonmonotone missing data](https://mlsanity.com/paper/2207.08535/) (2022)
- [Causal and Counterfactual Views of Missing Data Models](https://mlsanity.com/paper/2210.05558/) (2022)
- [Sufficient Identification Conditions and Semiparametric Estimation under Missing Not at Random Mechanisms](https://mlsanity.com/paper/2306.06443/) (2023)
- [Statistical and Causal Robustness for Causal Null Hypothesis Tests](https://mlsanity.com/paper/2310.10393/) (2023)
- [Markov-Restricted Analysis of Randomized Trials with Non-Monotone Missing Binary Outcomes](https://mlsanity.com/paper/2105.08868/) (2021)

## Abstract (arXiv)

We are grateful to the discussants, Levis and Kennedy [2025], Luo and Geng [2025], Wang and van der Laan [2025], and Yang and Kim [2025], for their thoughtful comments on our paper (Nabi et al., 2025). In this rejoinder, we summarize our main contributions and respond to each discussion in turn.