Causal Reinforcement Learning: An Instrumental Variable Approach

49 Pages Posted: 25 Feb 2021 Last revised: 6 Sep 2022

See all articles by Jin Li

Jin Li

Faculty of Business and Economics, The University of Hong Kong

Ye Luo

Faculty of Business and Economics, The University of Hong Kong

Xiaowei Zhang

The Hong Kong University of Science and Technology

Date Written: September 2, 2022

Abstract

In the standard data analysis framework, data is first collected (once for all), and then data analysis is carried out. Moreover, the data-generating process is typically assumed to be exogenous. This approach is natural when the data analyst has no impact on how the data is generated. The advancement of digital technology, however, has facilitated firms to learn from data and make decisions at the same time. As these decisions generate new data, the data analyst—a business manager or an algorithm—also becomes the data generator. This interaction generates a new type of bias—reinforcement bias—that exacerbates the endogeneity problem in static data analysis. Causal inference techniques ought to be incorporated into reinforcement learning to address such issues.

Keywords: Endogeneity, Markov Decision Process, Instrumental Variable, Reinforcement Bias, Reinforcement Learning, Q-Learning, Actor-Critic, Stochastic Approximation

Suggested Citation

Li, Jin and Luo, Ye and Zhang, Xiaowei, Causal Reinforcement Learning: An Instrumental Variable Approach (September 2, 2022). Available at SSRN: https://ssrn.com/abstract=3792824 or http://dx.doi.org/10.2139/ssrn.3792824

Jin Li

Faculty of Business and Economics, The University of Hong Kong ( email )

Pokfulam Road
Hong Kong, Pokfulam HK
Hong Kong

Ye Luo

Faculty of Business and Economics, The University of Hong Kong ( email )

Hong Kong

Xiaowei Zhang (Contact Author)

The Hong Kong University of Science and Technology ( email )

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