To illustrate the need for causal reasoning in artificial intelligence, Judea Pearl uses the example of diagnosing malaria. He argues that instead of merely identifying a statistical correlation between fever and malaria, machines must be able to understand the causal relationship—specifically, that malaria causes fever. Establishing this causal framework enables machines to ask counterfactual questions, which allows for the automated modification of the framework in response to specific interventions.