# Domain Adaptation under Missingness Shift

arXiv: https://arxiv.org/abs/2211.02093 · HTML: https://mlsanity.com/paper/2211.02093/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [A Unified View of Label Shift Estimation](https://mlsanity.com/paper/2003.07554/) (2020)
- [Dropout Training as Adaptive Regularization](https://mlsanity.com/paper/1307.1493/) (2013)
- [What's a good imputation to predict with missing values?](https://mlsanity.com/paper/2106.00311/) (2021)
- [Graphical Models for Processing Missing Data](https://mlsanity.com/paper/1801.03583/) (2018)
- [Mixture Proportion Estimation and PU Learning: A Modern Approach](https://mlsanity.com/paper/2111.00980/) (2021)
- [Linear predictor on linearly-generated data with missing values: non consistency and solutions](https://mlsanity.com/paper/2002.00658/) (2020)
- [Learning Models from Data with Measurement Error: Tackling Underreporting](https://mlsanity.com/paper/1901.09060/) (2019)
- [NeuMiss networks: differentiable programming for supervised learning with missing values](https://mlsanity.com/paper/2007.01627/) (2020)

## Built on by (most cited first, 13 of 13)

- [Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data](https://mlsanity.com/paper/2402.14989/) (2024)
- [Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms](https://mlsanity.com/paper/2305.19570/) (2023)
- [Evaluating Model Performance in Medical Datasets Over Time](https://mlsanity.com/paper/2305.13426/) (2023)
- [Large-Scale Study of Temporal Shift in Health Insurance Claims](https://mlsanity.com/paper/2305.05087/) (2023)
- [Robust prediction under missingness shifts](https://mlsanity.com/paper/2406.16484/) (2024)
- [Addressing Label Shift in Distributed Learning via Entropy Regularization](https://mlsanity.com/paper/2502.02544/) (2025)
- [ICYM2I: The illusion of multimodal informativeness under missingness](https://mlsanity.com/paper/2505.16953/) (2025)
- [Distribution Shift in Missing Data Imputation: A Risk-Based Perspective and Importance-Weighted Correction under MAR](https://mlsanity.com/paper/2602.06713/) (2026)
- [Domain Adaptation Under MNAR Missingness](https://mlsanity.com/paper/2504.00322/) (2025)
- [GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Values](https://mlsanity.com/paper/2508.14083/) (2025)
- [Weighted Conformal Prediction Provides Adaptive and Valid Mask-Conditional Coverage for General Missing Data Mechanisms](https://mlsanity.com/paper/2512.14221/) (2025)
- [The Effect of Missingness-Pattern Mismatch on Method Selection for Time-Series Classification: A Controlled Empirical Study](https://mlsanity.com/paper/2610.05368/) (2026)
- [When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch](https://mlsanity.com/paper/2610.20342/) (2026)

## Abstract (arXiv)

Rates of missing data often depend on record-keeping policies and thus may change across times and locations, even when the underlying features are comparatively stable. In this paper, we introduce the problem of Domain Adaptation under Missingness Shift (DAMS). Here, (labeled) source data and (unlabeled) target data would be exchangeable but for different missing data mechanisms. We show that if missing data indicators are available, DAMS reduces to covariate shift. Addressing cases where such indicators are absent, we establish the following theoretical results for underreporting completely at random: (i) covariate shift is violated (adaptation is required); (ii) the optimal linear source predictor can perform arbitrarily worse on the target domain than always predicting the mean; (iii) the optimal target predictor can be identified, even when the missingness rates themselves are not; and (iv) for linear models, a simple analytic adjustment yields consistent estimates of the optimal target parameters. In experiments on synthetic and semi-synthetic data, we demonstrate the promise of our methods when assumptions hold. Finally, we discuss a rich family of future extensions.