<ul><li>Judea Pearl’s causal calculus provides a formal framework for reasoning about causality.</li><li>The key operator is do(⋅), representing external intervention.</li><li>Structural equation modeling (SEM) is a cornerstone of counterfactual reasoning.</li><li>Counterfactuals are hypothetical scenarios: “What would Y have been if X were different?”</li><li>Causal inference underpins critical applications in AI.</li><li>Counterfactual reasoning provides a principled approach to ensuring fairness, enhancing explainability, and optimizing decisions in dynamic systems.</li><li>Causal inference in AI faces several theoretical and computational challenges.</li><li>One exciting frontier is counterfactual generative adversarial networks (CGANs).</li><li>Causality also has profound implications for scientific discovery.</li><li>There are several Python libraries designed for basic causal inference tasks.</li></ul>

Causality in AI and Counterfactual Reasoning

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