论文标题
在网络干扰下，几乎匹配的治疗效果估计
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference
论文作者
论文摘要
我们提出了一种匹配方法，该方法从随机实验中恢复了直接的治疗效果，在观测到的网络中连接单位，并且共享边缘的单位可能会影响彼此的结果。随机实验的传统治疗效应估计器是有偏见的，在这种情况下容易出错。我们的方法几乎完全匹配其邻里图中独特子图的数量。我们构建的比赛是可解释的和高质量的。我们的方法可以轻松扩展以适应其他单位级别协变量信息。我们从经验上表明，我们的方法的性能比其他现有方法的方法更好，同时产生有意义的，可解释的结果。
We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can potentially influence each others' outcomes. Traditional treatment effect estimators for randomized experiments are biased and error prone in this setting. Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs. The matches that we construct are interpretable and high-quality. Our method can be extended easily to accommodate additional unit-level covariate information. We show empirically that our method performs better than other existing methodologies for this problem, while producing meaningful, interpretable results.
