This paper studies the problem of optimally allocating treatments in the presence of spillover effects, using information from a (quasi-)experiment. I introduce a method that maximizes the sample analog of average social welfare when spillovers occur. I construct semi-parametric welfare estimators with known and unknown propensity scores and cast the optimization problem into a mixed-integer linear program, which can be solved using off-the-shelf algorithms. I derive a strong set of guarantees on regret, i.e., the difference between the maximum attainable welfare and the welfare evaluated at the estimated policy. The proposed method presents attractive features for applications: (i) it does not require network information of the target population
(ii) it exploits heterogeneity in treatment effects for targeting individuals
(iii) it does not rely on the correct specification of a particular structural model
and (iv) it accommodates constraints on the policy function. An application for targeting information on social networks illustrates the advantages of the method.