论文标题
在连续治疗干扰下对网络的因果推断
Causal Inference on Networks under Continuous Treatment Interference
论文作者
论文摘要
本文研究了单位治疗也影响其他单位的结果时，研究了干扰的情况。当干扰工作时，政策评估主要依赖于在集群干扰和二进制治疗下使用随机实验。取而代之的是，我们考虑在连续处理和网络干扰下进行非实验环境。特别是，我们通过指定接触网络处理的溢出效应为通过物理，社会或经济相互作用连接的单位收到的处理的加权平均值。我们提供基于广义的倾向得分估计器，以估计连续处理的直接和溢出效应。我们的估计器还允许考虑以异质强度为特征的不对称网络连接。为了展示这种方法，我们研究了溢出效应是否以及如何塑造农业市场政策干预措施的最佳水平。我们的结果表明，在这种情况下，忽视干扰可能会低估政策有效性的程度。
This paper investigates the case of interference, when a unit's treatment also affects other units' outcome. When interference is at work, policy evaluation mostly relies on the use of randomized experiments under cluster interference and binary treatment. Instead, we consider a non-experimental setting under continuous treatment and network interference. In particular, we define spillover effects by specifying the exposure to network treatment as a weighted average of the treatment received by units connected through physical, social or economic interactions. We provide a generalized propensity score-based estimator to estimate both direct and spillover effects of a continuous treatment. Our estimator also allows to consider asymmetric network connections characterized by heterogeneous intensities. To showcase this methodology, we investigate whether and how spillover effects shape the optimal level of policy interventions in agricultural markets. Our results show that, in this context, neglecting interference may underestimate the degree of policy effectiveness.
