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  "headline": "Causal AI: From Correlation to Causation with do-Calculus",
  "description": "Causal AI moves beyond correlation-based prediction to reason about cause and effect — enabling models to answer \"what if\" questions, make robust decisions under distribution shift, and avoid spurious correlations that break ML systems in production.",
  "dateCreated": "2026-05-24T02:49:13.588Z",
  "dateModified": "2026-05-24",
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  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
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      "name": "Causal Machine Learning: A Survey and Open Problems",
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