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 not
affect the outcome of another unit. Unfortunately, ignoring interference may
lead to biased policy evaluation and yield ineffective learned policies. For
example, treating influential individuals who have many friends can generate
positive spillover effects, thereby improving the overall performance of an
individualized treatment rule (ITR). We consider the problem of evaluating and
learning an optimal ITR under clustered network (or partial) interference where
clusters of units are sampled from a population and units may influence one
another within each cluster. Under this model, we propose an estimator that can
be used to evaluate the empirical performance of an ITR. We show that this
estimator is substantially more efficient than the standard inverse probability
weighting estimator, which does not impose any assumption about spillover
effects. We derive the finite-sample regret bound for a learned ITR, showing
that the use of our efficient evaluation estimator leads to the improved
performance of learned policies. Finally, we conduct simulation and empirical
studies to illustrate the advantages of the proposed methodology.

在存在干预关系的集群网络中，评估和学习个性化治疗规则的最优性能，提出了一个可用于评估个性化治疗规则经验性能的估计器，该估计器比标准的倒数概率加权估计器更有效，而且可以改善学习策略的性能。