Policy Choice in Time Series by Empirical Welfare Maximization

TL;DR

This paper introduces T-EWM, a new method for policy selection in dynamic multivariate time series settings, addressing challenges like time dependence and changing environments, with applications to Covid-19 restrictions.

Contribution

It develops the T-EWM framework for optimal policy learning in time series, including theoretical guarantees and practical implementation.

Findings

T-EWM can consistently learn optimal policies in time-varying environments.

Nonasymptotic bounds for welfare regret are derived.

Application to Covid-19 restriction policies demonstrates practical utility.

Abstract

This paper develops a novel method for policy choice in a dynamic setting where the available data is a multivariate time series. Overcoming challenges unique to time-series setting such as time-varying environments, history-dependent welfare, dynamic causal effects, and statistical dependence, we propose Time-series Empirical Welfare Maximization (T-EWM) methods. We characterize conditions for T-EWM to consistently learn optimal policies conditional or unconditinal on the time-series history, and derive nonasymptotic upper bounds for the welfare regrets. We illustrate a use of T-EWM for optimal restriction rules against Covid-19.