Making good decisions in business is about understanding the difference between prediction and causation. Predicting patterns is important, but thinking about causation is crucial for making decisions that benefit the business. It's essential for data scientists to recognize this distinction and focus on helping the business make decisions rather than just predicting. Causal inference is a key concept in this shift. For example, in a marketplace like Airbnb, ranking algorithms play a crucial role in finding matches. Evaluating these algorithms based on their ability to create better matches rather than just recreating past choices is the real decision that needs to be made. Ultimately, the goal is to increase bookings and revenue for the business.

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