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. When a company wants to predict what would happen if it were to make one decision versus another — such as whether a 10% discount or none is more likely to induce a customer to make a repeat purchase — it needs causal ML.

Our research on machine learning and AI and our experience helping companies apply causal ML points out a path to using the technology successfully. (See “The Research”) Companies will need the right expertise, too, and should boost employees’ literacy in causal ML.

What Causal ML Can and Cannot Do

Causal ML is a powerful tool, but managers may find the name misleading. The label “counterfactual prediction” would more accurately reflect what it does: predict outcomes based on hypothetical actions. The technology is best understood as a way to make better guesses rather than as a source of definitive answers. Framing it in this way can remind managers not to overinterpret the results.

It does this using causal inference, which looks at past results to understand cause-and-effect relationships among variables. But instead of focusing on why something happened, causal ML applies these relationships to predict the effects of interventions in new, forward-looking settings.

However, the method cannot explain why a causal relationship exists between a particular factor and the outcome it affects. For instance, a causal ML model might predict that reducing an R&D budget will decrease revenue, but it will not explain why that relationship exists or whether confounders — factors affecting both the decision and the outcome — might change and invalidate that prediction. Managers should use their domain expertise to evaluate whether a given prediction makes sense. This approach helps ensure that the model’s predictions are interpreted correctly and remain relevant to real-world decisions.

Like traditional machine learning, causal ML is most effective when managers have large volumes of data, their options are clearly defined, and the desired outcome is well understood. It is generally unsuitable for one-off decisions and in scenarios requiring intuition or creativity.

Choose the Right Problem — and Data

Causal ML is best at predicting the outcomes of straightforward decisions that are supported by ample historical data from internal and external sources. Questions about operations can be good candidates for the approach because they are made frequently and companies have a lot of data to support them.4 The following are examples of causal ML’s use in that context:

•  Booking.com collects data from thousands of hotel reservations every hour. Marketers at the company use causal ML to determine not only whether to give discounts but also which customers should get them.
•  Chocolate maker Lindt has extensive data about environmental conditions, equipment, packaging, and other factors that affect the quality of its world-famous truffles. Manufacturing managers use causal ML to help them fine-tune parameters such as the temperature of machines and the configurations of truffle molds.5
• Hitachi ABB Power Grids turned to causal ML to reduce failure rates in its semiconductor manufacturing process, using machine performance data. It was able to cut its yield loss by about half by identifying which combination of machines consistently produced the best-quality chips.6

At Novartis, managers who had been educated about the capabilities of different kinds of machine learning were able to identify several decision-making tasks where replacing traditional machine learning with causal ML offered significant benefits. They had asked a traditional ML model whether increasing the marketing budget would increase sales, but its predictions were not helping them decide how to allocate that budget. They decided to use causal ML to evaluate how different promotional campaigns might affect future sales. They used the predictions to distribute resources to the campaigns that were likely to be most effective.

A decision that is suitable for causal ML can be expressed as a number or a binary choice (such as an amount of revenue or buy/hold). It may also be framed as a question about which action to take: to allocate a marketing budget of $10,000 or $15,000 for the next quarter, or to offer a 10% discount or none on a product.7

Further, causal ML cannot effectively address every potential use case, even if it seems suitable for that on the surface. Confounders — the variables that affect both the outcome and the decision — introduce biases that affect predictions and must be accounted for. They can be challenging or impossible to test for, and they affect the accuracy of predictions. If, for example, data is available only for product sales during an economic upturn, predictions of product sales during a downturn would be less reliable.

When managers have determined what they want to decide, identified how they will measure the outcome, and affirmed that they have enough data, they can begin to work with data scientists to assemble and categorize that data to build their causal ML model. Business leaders and other individuals with domain knowledge are essential partners to data scientists and machine learning experts in building causal ML models that provide reliable results.

Training the model to capture complex cause-and-effect relationships requires data from at least a few dozen — and ideally, hundreds or thousands — of historical decisions. With massive amounts of data, the model can uncover connections between variables that may be unknown to managers or difficult to quantify. Less data leads to less-accurate predictions.

Broadly, causal ML requires three categories of data that were alluded to earlier: decisions, outcomes, and confounders. Decision data encompasses what managers have done in the past, such as the staffing levels or budgets they set, discounts they offered, investments they made, or processes they changed. Outcome data may include any measurable business result, such as sales volume, revenue growth, quality metrics, or productivity.

Confounders can come from internal or external sources. They may include economic conditions, workforce composition, and competitor behavior, and they can vary with the decision being made. For a marketing decision, the type of device customers use may be a confounder because those with more expensive smartphones may tend to spend more money whether or not they respond to an incentive.

For example, Neue Zürcher Zeitung, an international media company that publishes the largest-circulation newspaper in Switzerland, implemented causal ML to improve the effectiveness of editors’ content promotion decisions. The decision variable was whether an online article was promoted on one of two front pages that were served to readers. The outcome variable was a performance score that combined website traffic, reader engagement, and subscription signups. Confounders included time factors (such as the hour of the day), content characteristics (such as the article format), past performance indicators (including clicks), and past promotion decisions (including whether the article had been promoted elsewhere).