Practical do-Shapley Explanations with Estimand-Agnostic Causal Inference

• URL: http://arxiv.org/abs/2509.20211v1
• Date: Wed, 24 Sep 2025 15:04:25 GMT
• Title: Practical do-Shapley Explanations with Estimand-Agnostic Causal Inference
• Authors: Álvaro Parafita, Tomas Garriga, Axel Brando, Francisco J. Cazorla,
• Abstract summary: SHAP is one of the most popular explainability techniques, but often overlooks the causal structure of the problem.<n>We propose the use of estimand-agnostic approaches, which allow for the estimation of any identifiable query from a single model.<n>We also develop a novel algorithm to significantly accelerate its computation at a negligible cost.
• Score: 1.09610932276724
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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