# The Missing Indicator Method: From Low to High Dimensions

arXiv: https://arxiv.org/abs/2211.09259 · HTML: https://mlsanity.com/paper/2211.09259/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 62 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 18 of 18)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training](https://mlsanity.com/paper/2106.01342/) (2021)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III](https://mlsanity.com/paper/1907.08322/) (2019)
- [What's a good imputation to predict with missing values?](https://mlsanity.com/paper/2106.00311/) (2021)
- [On the consistency of supervised learning with missing values](https://mlsanity.com/paper/1902.06931/) (2019)
- [MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms](https://mlsanity.com/paper/2111.03187/) (2021)
- [Benchmarking missing-values approaches for predictive models on health databases](https://mlsanity.com/paper/2202.10580/) (2022)
- [Linear predictor on linearly-generated data with missing values: non consistency and solutions](https://mlsanity.com/paper/2002.00658/) (2020)
- [Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula](https://mlsanity.com/paper/2006.10829/) (2020)
- [Does imputation matter? Benchmark for predictive models](https://mlsanity.com/paper/2007.02837/) (2020)
- [Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks](https://mlsanity.com/paper/1912.12397/) (2019)
- [gcimpute: A Package for Missing Data Imputation](https://mlsanity.com/paper/2203.05089/) (2022)
- [Evolving imputation strategies for missing data in classification problems with TPOT](https://mlsanity.com/paper/1706.01120/) (2017)

## Built on by (most cited first, 19 of 19)

- [Conformal Prediction with Missing Values](https://mlsanity.com/paper/2306.02732/) (2023)
- [Imputation for prediction: beware of diminishing returns](https://mlsanity.com/paper/2407.19804/) (2024)
- [Interpretable Generalized Additive Models for Datasets with Missing Values](https://mlsanity.com/paper/2412.02646/) (2024)
- [Knockout: A simple way to handle missing inputs](https://mlsanity.com/paper/2405.20448/) (2024)
- [Leveraging Predictive Equivalence in Decision Trees](https://mlsanity.com/paper/2506.14143/) (2025)
- [Adaptive Optimization for Prediction with Missing Data](https://mlsanity.com/paper/2402.01543/) (2024)
- [Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution](https://mlsanity.com/paper/2601.04855/) (2026)
- [Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions in Transformer](https://mlsanity.com/paper/2504.14610/) (2025)
- [CoIFNet: A Unified Framework for Multivariate Time Series Forecasting with Missing Values](https://mlsanity.com/paper/2506.13064/) (2025)
- [When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values](https://mlsanity.com/paper/2507.13024/) (2025)
- [Model-Agnostic Dynamic Feature Selection with Uncertainty Quantification](https://mlsanity.com/paper/2508.02566/) (2025)
- [dnamite: A Python Package for Neural Additive Models](https://mlsanity.com/paper/2503.07642/) (2025)
- [To impute or not to impute: How machine learning modelers treat missing data](https://mlsanity.com/paper/2503.16644/) (2025)
- [Prediction Models That Learn to Avoid Missing Values](https://mlsanity.com/paper/2505.03393/) (2025)
- [AICO: Feature Significance Tests for Supervised Learning](https://mlsanity.com/paper/2506.23396/) (2025)
- [Deep Learning for Art Market Valuation](https://mlsanity.com/paper/2512.23078/) (2025)
- [Handling Covariate Mismatch in Collaborative Linear Prediction](https://mlsanity.com/paper/2602.02083/) (2026)
- [Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data](https://mlsanity.com/paper/2605.19641/) (2026)
- [Reliable Remediation Impact Prediction for Black-Box Security Ratings](https://mlsanity.com/paper/2607.16357/) (2026)

## Abstract (arXiv)

Missing data is common in applied data science, particularly for tabular data sets found in healthcare, social sciences, and natural sciences. Most supervised learning methods only work on complete data, thus requiring preprocessing such as missing value imputation to work on incomplete data sets. However, imputation alone does not encode useful information about the missing values themselves. For data sets with informative missing patterns, the Missing Indicator Method (MIM), which adds indicator variables to indicate the missing pattern, can be used in conjunction with imputation to improve model performance. While commonly used in data science, MIM is surprisingly understudied from an empirical and especially theoretical perspective. In this paper, we show empirically and theoretically that MIM improves performance for informative missing values, and we prove that MIM does not hurt linear models asymptotically for uninformative missing values. Additionally, we find that for high-dimensional data sets with many uninformative indicators, MIM can induce model overfitting and thus test performance. To address this issue, we introduce Selective MIM (SMIM), a novel MIM extension that adds missing indicators only for features that have informative missing patterns. We show empirically that SMIM performs at least as well as MIM in general, and improves MIM for high-dimensional data. Lastly, to demonstrate the utility of MIM on real-world data science tasks, we demonstrate the effectiveness of MIM and SMIM on clinical tasks generated from the MIMIC-III database of electronic health records.