Policy Targeting under Network Interference

This paper discusses the problem of estimating treatment allocation rules under network interference. I propose a method with several attractive features for applications: (i) it does not rely on the correct specification of a particular structural model; (ii) it exploits heterogeneity in treatment effects for targeting individuals; (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 strong 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. I provide a mixed-integer linear program formulation, 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.