Machine learning is now widely used to guide decisions in processes where gauging the probability of a specific outcome ' such as whether a customer will repay a loan ' is sufficient. But because the technology, as traditionally applied, relies on correlations to make predictions, the insights it offers managers is flawed, at best, when it comes to anticipating the impact of different choices on business outcomes.1 Consider leaders at a large company who must decide how much to invest in R&D in the coming year. Using traditional ML, they can ask what will happen when they increase their spending. They might find a strong correlation between higher levels of investment and higher revenue when the economy is growing. And they might conclude that, since economic conditions are favorable, they should increase the R&D budget. But should they really' If so, by how much' External factors, such as levels of consumer spending, technology spillover from competitors, and interest rates, also...
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