# Identification and Estimation of Causal Effects with Confounders Missing Not at Random

arXiv: https://arxiv.org/abs/2303.05878 · HTML: https://mlsanity.com/paper/2303.05878/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2023.

## Built on by (most cited first, 1 of 1)

- [Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding](https://mlsanity.com/paper/2410.06726/) (2024)

## Abstract (arXiv)

Making causal inferences from observational studies can be challenging when confounders are missing not at random. In such cases, identifying causal effects is often not guaranteed. Motivated by a real example, we consider a treatment-independent missingness assumption under which we establish the identification of causal effects when confounders are missing not at random. We propose a weighted estimating equation (WEE) approach for estimating model parameters and introduce three estimators for the average causal effect, based on regression, propensity score weighting, and doubly robust estimation. We evaluate the performance of these estimators through simulations, and provide a real data analysis to illustrate our proposed method.