The complex math of counterfactuals could help Spotify pick your next favorite song
“Causal reasoning is essential for machine learning,” says Nailong Zhang, software engineer at Meta. Meta uses causal inference in a machine learning model that manages the number and type of notifications Instagram needs to send to its users to keep them coming back.
Romila Pradhan, a data scientist at Purdue University in Indiana, uses counterfactuals to make automated decision-making more transparent. Organizations are now using machine learning models to choose who gets credit, a job, parole, even housing (and who doesn’t). Regulators have begun to require organizations to explain the outcome of many of these decisions to those affected. But reconstructing the steps performed by a complex algorithm is difficult.
Pradhan thinks counterfactuals can help. Suppose a bank’s machine learning model rejects your loan application and you want to know why. One way to answer this question is to use counterfactuals. Since the application was rejected in the real world, would it have been rejected in a fictional world where your credit history was different? What if you had a zip code, job, income, etc. different ? Building the capacity to answer these questions in future loan approval programs, Pradhan says, would give banks a way to offer customers reasons rather than a simple yes or no.
Counterfactuals are important because that’s how people think about different outcomes, says Pradhan: “They’re a good way to grasp explanations.”
They can also help companies predict people’s behavior. Because counterfactuals make it possible to infer what might be happening in a particular situation, not just on average, tech platforms can use it to classify people more accurately than ever before.
The same logic that can disentangle the effects of dirty water or lending decisions can be used to refine the impact of Spotify playlists, Instagram notifications, and ad targeting. If we play this song, will this user listen longer? If we show this image, will this person continue to scroll? “Companies want to figure out how to give recommendations to specific users rather than the average user,” says Gilligan-Lee.
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