Practical Do-Shapley Explanations with Estimand-Agnostic Causal Inference
Abstract
Among explainability techniques, SHAP stands out as one of the most popular, but often overlooks the causal structure of the problem. In response, do-SHAP employs interventional queries, but its reliance on estimands hinders its practical application. To address this problem, we propose the use of estimand-agnostic approaches, which allow for the estimation of any identifiable query from a single model, making do-SHAP feasible on complex graphs. We also develop a novel algorithm to significantly accelerate its computation at a negligible cost, as well as a method to explain inaccessible Data Generating Processes. We demonstrate the estimation and computational performance of our approach, and validate it on two real-world datasets, highlighting its potential in obtaining reliable explanations.
Cite
Text
Parafita et al. "Practical Do-Shapley Explanations with Estimand-Agnostic Causal Inference." Advances in Neural Information Processing Systems, 2025.Markdown
[Parafita et al. "Practical Do-Shapley Explanations with Estimand-Agnostic Causal Inference." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/parafita2025neurips-practical/)BibTeX
@inproceedings{parafita2025neurips-practical,
title = {{Practical Do-Shapley Explanations with Estimand-Agnostic Causal Inference}},
author = {Parafita, Álvaro and Garriga, Tomas and Brando, Axel and Cazorla, Francisco J.},
booktitle = {Advances in Neural Information Processing Systems},
year = {2025},
url = {https://mlanthology.org/neurips/2025/parafita2025neurips-practical/}
}