Feedback Detection for Live Predictors

Part of Advances in Neural Information Processing Systems 27 (NIPS 2014)

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Stefan Wager, Nick Chamandy, Omkar Muralidharan, Amir Najmi


A predictor that is deployed in a live production system may perturb the features it uses to make predictions. Such a feedback loop can occur, for example, when a model that predicts a certain type of behavior ends up causing the behavior it predicts, thus creating a self-fulfilling prophecy. In this paper we analyze predictor feedback detection as a causal inference problem, and introduce a local randomization scheme that can be used to detect non-linear feedback in real-world problems. We conduct a pilot study for our proposed methodology using a predictive system currently deployed as a part of a search engine.