Online Policy Learning and Inference by Matrix Completion
研究了在缺乏个性化协变量时如何通过矩阵补全方法进行在线决策，提出结合ε-贪心策略和在线梯度下降的策略学习算法，并开发了基于逆概率加权的在线去偏推断方法，应用于旧金山停车定价项目数据，效果优于基准策略。
Is it possible to make online decisions when personalized covariates are unavailable? We take a collaborative-filtering approach for decision-making based on collective preferences. By assuming low-dimensional latent features, we formulate the covariate-free decision-making problem as a matrix completion bandit. We propose a policy learning procedure that combines an ε-greedy policy for decision-making with an online gradient descent algorithm for bandit parameter estimation. Our novel two-phase design balances policy learning accuracy and regret performance. For policy inference, we develop an online debiasing method based on inverse propensity weighting and establish its asymptotic normality. Our methods are applied to data from the San Francisco parking pricing project, revealing intriguing discoveries and outperforming the benchmark policy.