Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling

• URL: http://arxiv.org/abs/2305.08062v2
• Date: Fri, 2 Jun 2023 20:52:40 GMT
• Title: Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling
• Authors: Yuta Saito, Qingyang Ren, Thorsten Joachims
• Abstract summary: We study off-policy evaluation of contextual bandit policies for large discrete action spaces. We propose a new estimator, called OffCEM, that is based on the conjunct effect model (CEM), a novel decomposition of the causal effect into a cluster effect and a residual effect. Experiments demonstrate that OffCEM provides substantial improvements in OPE especially in the presence of many actions.
• Score: 30.835774920236872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study off-policy evaluation (OPE) of contextual bandit policies for large discrete action spaces where conventional importance-weighting approaches suffer from excessive variance. To circumvent this variance issue, we propose a new estimator, called OffCEM, that is based on the conjunct effect model (CEM), a novel decomposition of the causal effect into a cluster effect and a residual effect. OffCEM applies importance weighting only to action clusters and addresses the residual causal effect through model-based reward estimation. We show that the proposed estimator is unbiased under a new condition, called local correctness, which only requires that the residual-effect model preserves the relative expected reward differences of the actions within each cluster. To best leverage the CEM and local correctness, we also propose a new two-step procedure for performing model-based estimation that minimizes bias in the first step and variance in the second step. We find that the resulting OffCEM estimator substantially improves bias and variance compared to a range of conventional estimators. Experiments demonstrate that OffCEM provides substantial improvements in OPE especially in the presence of many actions.

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