Dynamic Selection in Algorithmic Decision-making

• URL: http://arxiv.org/abs/2108.12547v3
• Date: Thu, 28 Sep 2023 01:21:27 GMT
• Title: Dynamic Selection in Algorithmic Decision-making
• Authors: Jin Li, Ye Luo, Xiaowei Zhang
• Abstract summary: This paper identifies and addresses dynamic selection problems in online learning algorithms with endogenous data. A novel bias (self-fulfilling bias) arises because the endogeneity of the data influences the choices of decisions. We propose an instrumental-variable-based algorithm to correct for the bias.
• Score: 9.172670955429906
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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