From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI
This work addresses conceptual confusion for researchers in data science, but it is incremental as it synthesizes existing ideas without new empirical results.
The paper tackles the problem of clarifying the role of counterfactuals in causal inference and explainable AI by introducing a formal definition and comparing their usage, evaluation, generation, and operationalization, aiming to identify cross-fertilization opportunities.
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.