A New Machine Learning Approach Answers What-If Questions
Causal ML enables managers to explore different options to improve decision-making.
Jing Jing Tsong/theispot.com
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
This makes the technology useful in many of the same business functions that use traditional ML, including product development, manufacturing, finance, human resources, and marketing.3 Traditional ML is still the go-to approach when the only goal is to make predictions — such as whether stock prices will rise or which products customers are most likely to buy.
References (10)
1. S. Feuerriegel, Y.R. Shrestha, G. von Krogh, et al., “Bringing Artificial Intelligence to Business Management,” Nature Machine Intelligence 4, no. 7 (July 2022): 611-613; and P. Hünermund, J. Kaminski, and C. Schmitt, “Causal Machine Learning and Business Decision-Making,” SSRN, updated Feb. 19, 2022, https://ssrn.com.
2. S. Feuerriegel, D. Frauen, V. Melnychuk, et al., “Causal Machine Learning for Predicting Treatment Outcomes,” Nature Medicine 30 (April 2024): 958-968; V. Chernozhukov, C. Hansen, N. Kallus, et al., “Applied Causal Inference Powered by ML and AI,” PDF file (pub. by the authors, July, 28, 2024), https:causalml-book.org; and C. Fernández-Loría and F. Provost, “Causal Decision-Making and Causal Effect Estimation Are Not the Same … and Why It Matters,” Informs Journal on Data Science 1, no. 1 (April-June 2022): 4-16.
