Practical do-Shapley Explanations with Estimand-Agnostic Causal Inference

Álvaro Parafita, Tomas Garriga, Axel Brando, Francisco J. Cazorla

Keywords: Shapley, Explainability, Causality, Attribution, SCM, Modeling

TL;DR: do-SHAP explanations made practical with Structural Causal Models and a novel algorithm to accelerate do-SHAP

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.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 12266