From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process

Interpretable Machine Learning needs formal standards like classical statistics: The paper argues that XAI currently lacks a unified framework and instead operates as a "loose collection of methods." By formalizing IML using statistical principles, the authors propose establishing rigorous standards similar to those in traditional statistics—a necessary step before deploying these systems in safety-critical applications like clinical diagnostics.

Current XAI methods may not adequately address causation: The paper's title emphasizes moving "from correlation to causation," suggesting that many existing interpretability methods identify correlations in data without establishing whether those correlations represent true causal relationships. This distinction is critical in healthcare and other high-stakes domains where understanding why a model makes a decision (causation) differs fundamentally from observing what patterns it uses (correlation).

Three critical questions must be answered for safe deployment: While not detailed in the abstract, the authors identify three key questions as "crucial for the success and adoption of IML in safety-critical settings." This suggests that responsible AI implementation requires answering specific, foundational questions about interpretability methods before they can be trusted in domains where wrong decisions have fatal consequences.

Interpretable ML should be treated as a rigorous scientific discipline: By positioning IML as a statistical process rather than an ad-hoc collection of techniques, the paper advocates for the same level of methodological rigor, validation, and peer scrutiny applied to classical statistics—implying that current XAI practices may be insufficiently rigorous for safety-critical applications.