Controlling Homophily in Social Network Regression Analysis by Machine Learning
针对社会网络研究中难以观测的潜在同质性导致的估计偏差，提出两种整合网络嵌入的方法：一种基于双机器学习，另一种基于神经网络，实验表明它们比现有方法更有效地减少遗漏变量偏误。
Across social science disciplines, empirical studies related to social networks have become the most popular research subjects in recent years. A frequently examined topic within these studies is the estimation of peer influence while controlling for homophily effects. However, although researchers may have access to all observable homophily variables, there is scarce literature addressing latent homophily effects stemming from unobservable features. Recent endeavors have demonstrated the efficacy of node embeddings derived from network structure in controlling latent homophily. Inspired by the network embedding research, this study introduces two methods that integrate node embeddings to better control latent homophily, particularly the nonlinear latent homophily effect. The first method uses double machine learning in the partially linear regression literature to alleviate estimation bias. The second method estimates peer influence effects directly by a novel neural network model. Our experimentation results show that our approaches outperform existing estimators in reducing the omitted variable bias due to homophily effects in network regression models. Theoretical analysis of two new estimation methods is also provided in this paper. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This research is supported by the National Research Foundation, Singapore under its Industry Alignment Fund - Pre-positioning (IAF-PP) Funding Initiative [Grant A-0003504-02-00]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0287 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0287 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .