# Towards Ground Truth Explainability on Tabular Data

arXiv: https://arxiv.org/abs/2007.10532 · HTML: https://mlsanity.com/paper/2007.10532/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

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

- [Longitudinal Distance: Towards Accountable Instance Attribution](https://mlsanity.com/paper/2108.10437/) (2021)
- [Influence of Data Dimensionality Reduction Methods on the Effectiveness of Quantum Machine Learning Models](https://mlsanity.com/paper/2511.03320/) (2025)
- [The Average Relative Entropy and Transpilation Depth determines the noise robustness in Variational Quantum Classifiers](https://mlsanity.com/paper/2603.21300/) (2026)
- [A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods](https://mlsanity.com/paper/2609.30397/) (2026)

## Abstract (arXiv)

In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering. Our current interest in synthetic data comes from recent work in explainability. Today's datasets are typically larger and more complex - requiring less interpretable models. In the setting of textitpost hoc explainability, there is no ground truth for explanations. Inspired by recent work in explaining image classifiers that does provide ground truth, we propose a similar solution for tabular data. Using copulas, a concise specification of the desired statistical properties of a dataset, users can build intuition around explainability using controlled data sets and experimentation. The current capabilities are demonstrated on three use cases: one dimensional logistic regression, impact of correlation from informative features, impact of correlation from redundant variables.