Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedback

arXiv:2302.09357v3 Announce Type: replace
Abstract: Endogeneity, i.e. the dependence of noise and covariates, is a common phenomenon in real data due to omitted variables, strategic behaviours, measurement errors etc. In contrast, the existing analyses of stochastic online linear regression with unbounded noise and linear bandits depend heavily on exogeneity, i.e. the independence of noise and covariates. Motivated by this gap, we study the over- and just-identified Instrumental Variable (IV) regression, specifically Two-Stage Least Squares, for stochastic online learning, and propose …

abstract arxiv contrast cs.lg data endogeneity errors etc feedback linear linear regression measurement noise real data regression stat.ml stochastic type variables