For the past decade, the AI and data science industry has been shaped by the dominant narrative of big data. From ad-tech to recommendation systems, the idea has been simple: more data leads to better models, so companies should focus on big data, scaling their infrastructure, deep learning, and tooling.
In the age of AI, every business leader hears the same message: use data to make better decisions. But most systems today still make brittle, black-box predictions without understanding the why behind the what. The reality? We're still in the early innings of AI-powered decision-making, and the biggest unlocks lie ahead.
Judea Pearl's Ladder of Causality has three levels: association (observing correlations), intervention (predicting effects of actions), and counterfactuals (imagining alternate outcomes). It's important because it shows that true causal understanding goes beyond data patterns, enabling reasoning about change and "what if" scenarios—essential for science, decision-making, and artificial intelligence.
The shift from correlation to causation isn't coming: it's already here. And the companies making it are seeing results that traditional data science can't match.
Causal AI is the science of understanding not just what happens, but why it happens and how to change it. Unlike traditional AI models which focus on correlations, causal AI emphasizes cause-and-effect relationships, enabling deeper insights, better decision-making and what-if simulation.