Instrumental Variable

Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity

In the standard data analysis framework, data is collected (once for all), and then data analysis is carried out. However, with the advancement of digital technology, decision-makers constantly analyze past data and generate new data through their …

Dynamic Selection in Algorithmic Decision-making

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 …