Asymptotic expansion for batched bandits
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In bandit algorithms, the randomly time-varying adaptive experimental design makes it difficult to apply traditional limit theorems to off-policy evaluation of the treatment effect. Moreover, the normal approximation by the central limit theorem becomes unsatisfactory for lack of information due to the small sample size of the inferior arm. To resolve this issue, we introduce a backwards asymptotic expansion method and prove the validity of this scheme based on the partial mixing, that was originally introduced for the expansion of the distribution of a functional of a jump-diffusion process in a random environment. The theory is generalized in this paper to incorporate the backward propagation of random functions in the bandit algorithm. Besides the analytical validation, the simulation studies also support the new method. Our formulation is general and applicable to nonlinearly parametrized differentiable statistical models having an adaptive design.
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