# Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding

arXiv: https://arxiv.org/abs/2410.06726 · HTML: https://mlsanity.com/paper/2410.06726/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [Graphical Models for Processing Missing Data](https://mlsanity.com/paper/1801.03583/) (2018)
- [Causal inference with confounders missing not at random](https://mlsanity.com/paper/1702.03951/) (2017)
- [Identification and Estimation of Causal Effects with Confounders Missing Not at Random](https://mlsanity.com/paper/2303.05878/) (2023)
- [Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach](https://mlsanity.com/paper/2311.09015/) (2023)

## Abstract (arXiv)

We report assumption-free bounds for any contrast between the probabilities of the potential outcome under exposure and non-exposure when the confounders are missing not at random. We assume that the missingness mechanism is outcome-independent. We also report a sensitivity analysis method to complement our bounds.