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Tomas Garriga

2 accepted papers

2026

Exactly Computing do-Shapley Values

ICML 2026poster

Structural Causal Models (SCM) are a powerful framework for describing complicated dynamics across the natural sciences. A particularly elegant way of interpreting SCMs is do-Shapley, a game-theoretic method of quantifying the average effect of $d$ variables across exponentially many interventions. …

Cited by 0SourceScholar
2025

Practical do-Shapley Explanations with Estimand-Agnostic Causal Inference

NeurIPS 2025spotlight

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 e…

Cited by 0SourceScholar