Doubly Robust Estimator for Off-Policy Evaluation with Large Action Spaces

Autor:	Shimizu, Tatsuhiro, Forastiere, Laura
Rok vydání:	2023
Předmět:	Statistics - Machine Learning Computer Science - Artificial Intelligence Computer Science - Information Retrieval Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We study Off-Policy Evaluation (OPE) in contextual bandit settings with large action spaces. The benchmark estimators suffer from severe bias and variance tradeoffs. Parametric approaches suffer from bias due to difficulty specifying the correct model, whereas ones with importance weight suffer from variance. To overcome these limitations, Marginalized Inverse Propensity Scoring (MIPS) was proposed to mitigate the estimator's variance via embeddings of an action. Nevertheless, MIPS is unbiased under the no direct effect, which assumes that the action embedding completely mediates the effect of an action on a reward. To overcome the dependency on these unrealistic assumptions, we propose a Marginalized Doubly Robust (MDR) estimator. Theoretical analysis shows that the proposed estimator is unbiased under weaker assumptions than MIPS while reducing the variance against MIPS. The empirical experiment verifies the supremacy of MDR against existing estimators with large action spaces. Comment: 14 pages, 8 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.03443 Zobrazit plný text záznamu View this record from Arxiv