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

Counterfactuals serve different purposes in causal inference versus explainable AI: While both fields use "what-if" scenarios, causal inference uses counterfactuals to understand cause-and-effect relationships in the real world, whereas explainable AI uses them to help users understand why an AI system made a specific decision about them.

The same tool can be interpreted differently depending on context: The paper establishes a formal definition of counterfactuals that works across both fields, but notes that how these scenarios are evaluated and used differs significantly—meaning practitioners need to be clear about which framework they're operating within to avoid misapplying findings.

There are practical differences in how counterfactuals are generated and validated: The paper highlights that causal inference and XAI have distinct methods for creating and testing counterfactuals, suggesting that responsible AI practitioners should understand these differences rather than assuming one approach works universally.

Cross-disciplinary learning could improve both fields: By identifying where causal inference and XAI diverge, the paper suggests opportunities for each field to adopt better practices from the other—potentially leading to more rigorous and trustworthy AI explanations that are grounded in causal reasoning.

Understanding counterfactuals matters for AI accountability: Since counterfactuals are increasingly used to explain AI decisions to users and regulators, clarifying how they work and their limitations is essential for building responsible AI systems that people can actually trust and verify.