Machine learning as a natural experiment: Application to fashion e-commerce

Machine learning algorithms are increasingly being used in decision making. Web companies, car-sharing services, and courts rely on algorithms to supply content, set prices, and estimate recidivism rates. This column introduces a method for predicting counterfactual performance of new algorithms using data from older algorithms as a natural experiment. When applied to a fashion e-commerce service, the method increases the click through rate and improved the recommendations algorithm.

Decision making using prediction by machine learning (ML) algorithms is becoming increasingly widespread (Athey and Imbens 2017, Mullainathan and Spiess 2017). For instance, Amazon, Facebook, Google, Microsoft, Netflix, and other web companies apply machine learning to problems such as personalising ads and content (movies, music, news, etc.), determining prices, and ranking search results. The prices set by car sharing services such as Uber, Lyft, and DiDi are also based on proprietary algorithms based on information about supply and demand at each point in time and location (Cohen et al. 2016).

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