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.
@article{arxiv.1310.2931,
title = {Feedback Detection for Live Predictors},
author = {Stefan Wager and Nick Chamandy and Omkar Muralidharan and Amir Najmi},
journal= {arXiv preprint arXiv:1310.2931},
year = {2014}
}
Comments
Advances in Neural Information Processing Systems (NIPS), 2014