Control Variates for Slate Off-Policy Evaluation

Nikos Vlassis, Ashok Chandrashekar, Fernando Amat, Nathan Kallus

Keywords: off-policy evaluation, combinatorial actions, slate bandits, control variates

Abstract: We study the problem of off-policy evaluation from batched contextual bandit data with multidimensional actions, often termed slates. The problem is common to recommender systems and user-interface optimization, and it is particularly challenging because of the combinatorially-sized action space. Swaminathan et al. (2017) have proposed the pseudoinverse (PI) estimator under the assumption that the conditional mean rewards are additive in actions. Using control variates, we consider a large class of unbiased estimators that includes as specific cases the PI estimator and (asymptotically) its self-normalized variant. By optimizing over this class, we obtain new estimators with risk improvement guarantees over both the PI and the self-normalized PI estimators. Experiments with real-world recommender data as well as synthetic data validate these improvements in practice.

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TL;DR: Using control variates, we develop improved estimators for off-policy evaluation of contextual slate bandits

Code: https://github.com/fernandoamat/slateOPE

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/control-variates-for-slate-off-policy/code)