Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding
Methodology
2024-11-04 v2 Machine Learning
Machine Learning
Abstract
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.
Cite
@article{arxiv.2410.06726,
title = {Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding},
author = {Jose M. Peña},
journal= {arXiv preprint arXiv:2410.06726},
year = {2024}
}