# Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?

arXiv: https://arxiv.org/abs/2304.11749 · HTML: https://mlsanity.com/paper/2304.11749/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 23 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)
- [Debugging Tests for Model Explanations](https://mlsanity.com/paper/2011.05429/) (2020)
- [Generalized and Scalable Optimal Sparse Decision Trees](https://mlsanity.com/paper/2006.08690/) (2020)
- [AlphaClean: Automatic Generation of Data Cleaning Pipelines](https://mlsanity.com/paper/1904.11827/) (2019)
- [GAM Changer: Editing Generalized Additive Models with Interactive Visualization](https://mlsanity.com/paper/2112.03245/) (2021)
- [Causal Inference: A Missing Data Perspective](https://mlsanity.com/paper/1712.06170/) (2017)

## Built on by (most cited first, 6 of 6)

- [Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework](https://mlsanity.com/paper/2311.11055/) (2023)
- [Daily Physical Activity Monitoring -- Adaptive Learning from Multi-source Motion Sensor Data](https://mlsanity.com/paper/2405.16395/) (2024)
- [GAMformer: Bridging Tabular Foundation Models and Interpretable Machine Learning](https://mlsanity.com/paper/2410.04560/) (2024)
- [Ocular-Induced Abnormal Head Posture: Diagnosis and Missing Data Imputation](https://mlsanity.com/paper/2510.05649/) (2025)
- [Imputation of Unknown Missingness in Sparse Electronic Health Records](https://mlsanity.com/paper/2602.20442/) (2026)
- [SleuthBench: Benchmarking Statistical LLM Evaluation Using Tabular Hidden Signals](https://mlsanity.com/paper/2609.34228/) (2026)

## Abstract (arXiv)

Missing values are a fundamental problem in data science. Many datasets have missing values that must be properly handled because the way missing values are treated can have large impact on the resulting machine learning model. In medical applications, the consequences may affect healthcare decisions. There are many methods in the literature for dealing with missing values, including state-of-the-art methods which often depend on black-box models for imputation. In this work, we show how recent advances in interpretable machine learning provide a new perspective for understanding and tackling the missing value problem. We propose methods based on high-accuracy glass-box Explainable Boosting Machines (EBMs) that can help users (1) gain new insights on missingness mechanisms and better understand the causes of missingness, and (2) detect -- or even alleviate -- potential risks introduced by imputation algorithms. Experiments on real-world medical datasets illustrate the effectiveness of the proposed methods.