内生性
因果推理
网络分析
因果模型
社会网络分析
因果分析
计算机科学
共线性
缺少数据
数据科学
社交网络(社会语言学)
计量经济学
同时性
领域(数学)
推论
工具变量
人工智能
机器学习
统计
经济
数学
工程类
物理
万维网
电气工程
经典力学
纯数学
社会化媒体
作者
Weihua An,Roberson Beauvile,Benjamin Rosche
标识
DOI:10.1146/annurev-soc-030320-102100
摘要
Fueled by recent advances in statistical modeling and the rapid growth of network data, social network analysis has become increasingly popular in sociology and related disciplines. However, a significant amount of work in the field has been descriptive and correlational, which prevents the findings from being more rigorously translated into practices and policies. This article provides a review of the popular models and methods for causal network analysis, with a focus on causal inference threats (such as measurement error, missing data, network endogeneity, contextual confounding, simultaneity, and collinearity) and potential solutions (such as instrumental variables, specialized experiments, and leveraging longitudinal data). It covers major models and methods for both network formation and network effects and for both sociocentric networks and egocentric networks. Lastly, this review also discusses future directions for causal network analysis.
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