Feedback Detection for Live Predictors
Stefan Wager, Nick Chamandy, Omkar Muralidharan, and Amir Najmi

TL;DR
This paper introduces a causal inference-based method with local randomization to detect feedback loops in live predictors, demonstrated through a pilot study in a search engine system.
Contribution
It presents a novel approach to identify non-linear predictor feedback in real-world systems using local randomization techniques.
Findings
Effective detection of feedback loops demonstrated in a search engine system
Method can identify non-linear feedback in live predictors
Pilot study validates practical applicability
Abstract
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.
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Taxonomy
TopicsMachine Learning and Algorithms · Data Stream Mining Techniques · Machine Learning and Data Classification
MethodsCausal inference
