From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI

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Date

19 mai 2025

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Type de document
Périmètre
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  • 2505.13324
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arXiv - économie

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arXiv

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Cornell University

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Statistics - Machine Learning Computer Science - Artificial Intelligence Computer Science - Machine Learning Economics - Econometrics Statistics - Methodology

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Tsʻeu Tzʻu Ci Tse

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Galit Shmueli et al., « From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI », arXiv - économie

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Counterfactuals play a pivotal role in the two distinct data science fields of causal inference (CI) and explainable artificial intelligence (XAI). While the core idea behind counterfactuals remains the same in both fields--the examination of what would have happened under different circumstances--there are key differences in how they are used and interpreted. We introduce a formal definition that encompasses the multi-faceted concept of the counterfactual in CI and XAI. We then discuss how counterfactuals are used, evaluated, generated, and operationalized in CI vs. XAI, highlighting conceptual and practical differences. By comparing and contrasting the two, we hope to identify opportunities for cross-fertilization across CI and XAI.

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