A New Machine Learning Approach Answers What-If Questions

Causal ML enables managers to explore different options to improve decision-making.

- By Stefan Feuerriegel, Yash Raj Shrestha, and Georg von Krogh

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 influence revenue growth. Comparing how different levels of investment might affect revenue while considering these other variables is useful for the manager who is trying to determine the R&D budget that will deliver the greatest benefit to the company.

Causal ML — an emerging area of machine learning — can help to answer such what-if questions through causal inference. Similar to how marketers use A/B tests to infer which of two ads is likely to generate more sales, causal ML can inform what might happen if managers were to take a particular action.2