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While there now exists a large literature on policy evaluation and learning,much of prior work assumes that the treatment assignment of one unit does notaffect the outcome of another unit. Unfortunately, ignoring interference maylead to biased policy evaluation and yield ineffective learned policies. Forexample, treating influential individuals who have many friends can generatepositive spillover effects, thereby improving the overall performance of anindividualized treatment rule (ITR). We consider the problem of evaluating andlearning an optimal ITR under clustered network (or partial) interference whereclusters of units are sampled from a population and units may influence oneanother within each cluster. Under this model, we propose an estimator that canbe used to evaluate the empirical performance of an ITR. We show that thisestimator is substantially more efficient than the standard inverse probabilityweighting estimator, which does not impose any assumption about spillovereffects. We derive the finite-sample regret bound for a learned ITR, showingthat the use of our efficient evaluation estimator leads to the improvedperformance of learned policies. Finally, we conduct simulation and empiricalstudies to illustrate the advantages of the proposed methodology.

Individualized Policy Evaluation and Learning under Clustered Network Interference