Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect

Ojash Neopane, Aaditya Ramdas, Aarti Singh

TL;DR: We study the problem of adaptively estimation the average treatment effect in a randomized trial, with an emphasis on a non-asymptotic analysis, and propose an approach which provides exponential improvements to previously proposed algorithms.

Abstract:

Estimation of the Average Treatment Effect (ATE) is a core problem in causal inference with strong connections to Off-Policy Evaluation in Reinforcement Learning. This paper considers the problem of adaptively selecting the treatment allocation probability in order to improve estimation of the ATE. The majority of prior work on adaptive ATE estimation focus on asymptotic guarantees, and in turn overlooks important practical considerations such as the difficulty of learning the optimal treatment allocation as well as hyper-parameter selection. Existing non-asymptotic methods are limited by poor empirical performance and exponential dependence on problem parameters. In order to address these gaps, we propose and analyze the Clipped Second Moment Tracking (ClipSMT) algorithm, a variant of an existing algorithm with strong asymptotic optimality guarantees, and provide finite sample bounds on its Neyman regret. Our analysis shows that, in the superpopulation setting, ClipSMT achieves exponential improvements in Neyman regret on two fronts: improving the dependence on $T$ from $O(\sqrt{T})$ to $O(\log T)$, as well as reducing the exponential dependence on problem parameters to a polynomial dependence---although the setting we consider is slightly less general. We conclude with simulations which show the marked improvement of \clipSMT over existing approaches.

Submission Number: 1613