Variational Approach to Optimal IPS Estimator for Multi-logger Off-Policy Evaluation

We study off-policy evaluation (OPE) in contextual bandits with data collected from multiple logging policies. Inverse propensity scoring (IPS) is a standard approach to OPE and extends naturally to the multi-logger setting. However, as highlighted by Agarwal et al. [2017], there appears to be no IPS estimator that consistently outperforms the others in this setting. We resolve this dilemma by deriving an optimal IPS estimator with sample-dependent weights that minimize variance subject to unbiasedness. Using a variational calculus approach, we obtain closed-form optimal weights, yielding an estimator that is unbiased and achieves asymptotically optimal variance within an weighted-IPS estimator class. Experiments on benchmark datasets confirm this theoretical resolution in practice, showing that our estimator consistently outperforms existing multi-logger IPS methods. We also extend our estimator to a doubly robust form by incorporating a conditional reward estimator. We compare the resulting DR extension with the semiparametrically efficient DR estimator of Kallus et al. [2021], both theoretically and empirically. We show that the two estimators achieve the same asymptotic variance when the conditional reward estimator is consistent, while our estimator can retain semiparametric efficiency under certain forms of reward misspecification. Empirically, the proposed DR estimator achieves lower variance than the existing DR baseline.