Predictive gains, causal losses: why interventional competence cannot be learned from data alone

This research paper argues that machine learning models trained purely on observational data cannot reliably learn causal interventional relationships, only predictive associations. The work has implications for enterprise AI systems in regulated industries where understanding cause-and-effect (e.

This research paper argues that machine learning models trained purely on observational data cannot reliably learn causal interventional relationships, only predictive associations. The work has implications for enterprise AI systems in regulated industries where understanding cause-and-effect (e.g., treatment efficacy, policy impact) is critical for decision-making, not just prediction.

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