  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.
