Bargagli-Stoffi Falco J, Tortú Costanza, Forastiere Laura
Department of Biostatistics, University of California, Los Angeles.
Management and Healthcare Laboratory, Sant'Anna School for Advanced Studies.
Ann Appl Stat. 2025 Mar;19(1):28-55. doi: 10.1214/24-aoas1913. Epub 2025 Mar 17.
The bulk of causal inference studies rule out the presence of interference between units. However, in many real-world scenarios, units are interconnected by social, physical, or virtual ties, and the effect of the treatment can spill from one unit to other connected individuals in the network. In this paper, we develop a machine learning method that uses tree-based algorithms and a Horvitz-Thompson estimator to assess the heterogeneity of treatment and spillover effects with respect to individual, neighborhood, and network characteristics in the context of clustered networks and interference within clusters. The proposed network causal tree (NCT) algorithm has several advantages. First, it allows the investigation of the heterogeneity of the treatment effect, avoiding potential bias due to the presence of interference. Second, understanding the heterogeneity of both treatment and spillover effects can guide policymakers in scaling up interventions, designing targeting strategies, and increasing cost-effectiveness. We investigate the performance of our NCT method using a Monte Carlo simulation study and illustrate its application to assess the heterogeneous effects of information sessions on the uptake of a new weather insurance policy in rural China.
大多数因果推断研究排除了个体间存在干扰的情况。然而，在许多现实世界场景中，个体通过社会、物理或虚拟联系相互连接，治疗效果可能会从一个个体传播到网络中其他有联系的个体。在本文中，我们开发了一种机器学习方法，该方法使用基于树的算法和霍维茨 - 汤普森估计量，在聚类网络和聚类内干扰的背景下，评估治疗效果和溢出效应在个体、邻域和网络特征方面的异质性。所提出的网络因果树（NCT）算法具有几个优点。首先，它允许研究治疗效果的异质性，避免因存在干扰而产生的潜在偏差。其次，了解治疗效果和溢出效应的异质性可以指导政策制定者扩大干预措施、设计靶向策略并提高成本效益。我们使用蒙特卡罗模拟研究来调查我们的NCT方法的性能，并说明其在评估信息会议对中国农村地区新天气保险政策采用情况的异质效应方面的应用。
