Originally published in MIT Technology Review, April 4, 2023.
A new kind of machine-learning model built by a team of researchers at the music-streaming firm Spotify captures for the first time the complex math behind counterfactual analysis, a precise technique that can be used to identify the causes of past events and predict the effects of future ones.
The model, described earlier this year in the scientific journal Nature Machine Intelligence, could improve the accuracy of automated decision making, especially personalized recommendations, in a range of applications from finance to health care.
The basic idea behind counterfactuals is to ask what would have happened in a situation had certain things been different. It’s like rewinding the world, changing a few crucial details, and then hitting play to see what happens. By tweaking the right things, it’s possible to separate true causation from correlation and coincidence.
“Understanding cause and effect is super important for decision making,” says Ciaran Gilligan-Lee, leader of the Causal Inference Research Lab at Spotify, who co-developed the model. “You want to understand what impact a choice you take now will have on the future.”
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