Policy Targeting under Network Interference

This paper discusses the problem of estimating individualized treatment allocation rules under network interference. I propose a method with several attractive features for applications: the method (i) exploits heterogeneity in treatment and spillover effects to construct targeting rules; (ii) it does not rely on the correct specification of a particular structural model; (iii) it accommodates arbitrary constraints on the policy function and capacity constraints on the number of treated units, and (iv) it can also be implemented when network information is not accessible to policy-makers. I establish a set of guarantees on the utilitarian regret, i.e., the difference between the average social welfare attained by the estimated policy function and the maximum attainable welfare, in the presence of network interference. The proposed method achieves the min-max regret-optimal rate in scenarios of practical and theoretical interest. I provide a linear program formulation under interference which can be solved using off-the-shelf algorithms. I discuss the empirical performance in simulations and illustrate the advantages of the method for targeting information on social networks.