Dynamic Selection in Algorithmic Decision-making
It addresses bias in algorithmic decision-making for online learning systems, offering a novel correction method with theoretical guarantees.
The paper tackles dynamic selection bias in online learning algorithms with endogenous data, proposing an instrumental-variable-based method that achieves true parameter estimation and low logarithmic-like regret, with proven statistical inference via a central limit theorem.
This paper identifies and addresses dynamic selection problems in online learning algorithms with endogenous data. In a contextual multi-armed bandit model, a novel bias (self-fulfilling bias) arises because the endogeneity of the data influences the choices of decisions, affecting the distribution of future data to be collected and analyzed. We propose an instrumental-variable-based algorithm to correct for the bias. It obtains true parameter values and attains low (logarithmic-like) regret levels. We also prove a central limit theorem for statistical inference. To establish the theoretical properties, we develop a general technique that untangles the interdependence between data and actions.