mlsanityData to 7 Oct 2026

Imputation Uncertainty in Interpretable Machine Learning Methods

arXiv 2512.17689 ↗First posted 19 Dec 2025stat.ML cs.LG stat.MEcited by 0

Not on the map. Why?

Abstract (arXiv)

In real data, missing values occur frequently, which affects the interpretation with interpretable machine learning (IML) methods. Recent work considers bias and shows that model explanations may differ between imputation methods, while ignoring additional imputation uncertainty and its influence on variance and confidence intervals. We therefore compare the effects of different imputation methods on the confidence interval coverage probabilities of the IML methods permutation feature importance, partial dependence plots and Shapley values. We show that single imputation leads to underestimation of variance and that, in most cases, only multiple imputation is close to nominal coverage.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 35 references that are in the map. First posted on arXiv 19 Dec 2025.

Builds on

11 of 35 references.

References · 11

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2018All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneouslycited by 364
  4. 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 343
  5. 2020Reliable Post hoc Explanations: Modeling Uncertainty in Explainabilitycited by 125
  6. 2020Efficient nonparametric statistical inference on population feature importance using Shapley valuescited by 61
  7. 2021Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Processcited by 24
  8. 2022Classification of datasets with imputed missing values: does imputation quality matter?cited by 21
  9. 2024Explainability of Machine Learning Models under Missing Datacited by 5
  10. 2019From interpretability to inference: an estimation framework for universal approximatorscited by 3
  11. 2016Bootstrap Inference when Using Multiple Imputationcited by 2

Built on by

Not cited yet.