Identifying the root causes of outcomes is not causal AI’s only advantage; it also makes it possible to model interventions that can change those outcomes, by using causal AI algorithms to ask what-if ...

The graphical saliency of its causal interpretation makes path analysis a dependable producer of debate on causality. This week's readings begins with a debate led by David Freedman, which focuses ...

This courses introduces causal inference methods, primarily using probabilistic graphical models, to identify and estimate counterfactual quantities as functions of observational data. We will discuss ...

The Evolution of Temporal Causal Analysis, Sree Charanreddy Pothireddi explores a crucial aspect of artificial intelligence and data science in his latest research, focusing on delayed causal effects ...

Causal AI for predicting clinical efficacy is currently transforming the pharma business model. The gold standard for establishing causality is the prospective, randomized, placebo-controlled ...