中文摘要
本文利用一项（准）实验的信息，研究存在溢出效应情形下如何最优配置处理的问题。我提出一种方法，可在发生溢出效应时最大化平均社会福利的样本对应值。我分别针对倾向得分已知和未知的情形，构建了半参数福利估计量，并将优化问题转化为可用现成算法求解的混合整数线性规划问题。我推导出一组关于遗憾（regret，即可达到的最大福利与在估计政策下所评估福利之间的差异）的强有力保证。所提出的方法在应用中具有以下优点：（i）不需要目标人群的网络信息；（ii）利用处理效应的异质性对个体进行精准配置；（iii）不依赖于某一特定结构模型的正确设定；（iv）允许对政策函数施加约束。一项在社交网络中精准投放信息的应用展示了该方法的优势。
Abstract
Abstract This article 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 analogue 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.