Dynamic Policy Evaluation and Learning with Spatio-temporal Data

2026-09-14

AI Summary

本文解决了时空数据下的动态策略评估与学习问题，通过开发半参数加性模型和稳定估计器来处理空间溢出和时间延续效应，并应用于伊拉克经济援助分配。

Abstract

Although sequential decision-making is ubiquitous across domains, policy evaluation and learning with spatio-temporal data remain challenging due to spatial spillover and temporal carryover effects. We develop methods for evaluating and learning individualized dynamic policies under spatio-temporal interference. Under a semiparametric additive outcome model that allows for complex spillover and carryover effects, we consider a family of stabilized estimators for evaluating the performance of a given individualized policy. From this family, we select a data-adaptive optimal estimator that minimizes the asymptotic variance. We then derive the asymptotic distribution of the proposed policy evaluation estimator, and establish the finite-sample regret bounds of our policy learning estimator. We further propose a statistical test to select the complexity of the semiparametric additive model by determining the appropriate order of interactions. Through simulations, we assess the finite-sample performance of our estimators and the validity of the proposed test. Our motivating application examines the optimal allocation of economic aid in Iraq from February 2007 to July 2008. Drawing on declassified conflict data, we study the weekly assignment of aid projects across districts and find that reallocating aid away from regions with a persistently high level of violence can substantially reduce insurgent attacks.

Problem

• spatio-temporal data
• policy evaluation
• dynamic policies
• spillover effects
• carryover effects

Innovation

• spatio-temporal interference
• semiparametric additive outcome model
• stabilized estimators
• asymptotic variance minimization
• data-adaptive optimal estimator