You Can Learn Causality from Data—With a Model 🎓

Judea Pearl asserts that simply gathering data is not enough to grasp causality. Instead, we need to create causal models that incorporate assumptions and establish relationships between variables.

Through causal modeling, we can identify key insights and patterns to better understand cause and effect. This allows us to answer questions such as "When A happens, what can we expect for B?" and make informed decisions based on data-driven evidence.