Asymptotically Best Causal Effect Identification with Multi-Armed Bandits

In this talk, I will present a method for selecting the causal identification formula with lowest asymptotic variance among a set of available ones. The method assumes an online setting in which the investigator may alter the data collection mechanism in a data-dependent way with the aim of identifying the formula in as few samples as possible, and formalizes this setting using the best-arm-identification bandit framework where the standard goal of learning the arm with the lowest loss is replaced with the goal of learning the arm that will produce the best estimate.