# A Versatile Estimation Procedure without Estimating the Nonignorable Missingness Mechanism

arXiv: https://arxiv.org/abs/1907.03682 · HTML: https://mlsanity.com/paper/1907.03682/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [Sufficient Identification Conditions and Semiparametric Estimation under Missing Not at Random Mechanisms](https://mlsanity.com/paper/2306.06443/) (2023)
- [Off-Policy Evaluation Under Nonignorable Missing Data](https://mlsanity.com/paper/2507.06961/) (2025)
- [SADA: Safe and Adaptive Aggregation of Multiple Black-Box Predictions in Semi-Supervised Learning](https://mlsanity.com/paper/2509.21707/) (2025)
- [DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation](https://mlsanity.com/paper/2408.02045/) (2024)

## Abstract (arXiv)

We consider the estimation problem in a regression setting where the outcome variable is subject to nonignorable missingness and identifiability is ensured by the shadow variable approach. We propose a versatile estimation procedure where modeling of missingness mechanism is completely bypassed. We show that our estimator is easy to implement and we derive the asymptotic theory of the proposed estimator. We also investigate some alternative estimators under different scenarios. Comprehensive simulation studies are conducted to demonstrate the finite sample performance of the method. We apply the estimator to a children's mental health study to illustrate its usefulness.