Causal AI: From Correlation to Causation with do-Calculus

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## TL;DR
Causal AI: From Correlation to Causation with do-Calculus: Causal inference in AI asks whether and how variables affect each other, not only whether they are correlated.

## Core Explanation
Causal AI combines graphical models, potential outcomes, interventions, counterfactuals, and machine learning. The goal is to estimate effects and support decisions under assumptions that are stated and testable when possible.

## Further Reading

- [Causality: Models, Reasoning, and Inference](http://bayes.cs.ucla.edu/BOOK-2K/)
- [Causal Inference Using Potential Outcomes](https://doi.org/10.1198/016214504000001880)
- [Double/debiased machine learning for treatment and structural parameters](https://academic.oup.com/ectj/article/21/1/C1/5056401)