Clark Duncan A, Handcock Mark S
Department of Statistics & Data Science, University of California - Los Angeles, Los Angeles, CA, USA.
J R Stat Soc Ser A Stat Soc. 2024 Jan 25;187(3):772-795. doi: 10.1093/jrsssa/qnae001. eCollection 2024 Aug.
Claiming causal inferences in network settings necessitates careful consideration of the often complex dependency between outcomes for actors. Of particular importance are treatment spillover or outcome interference effects. We consider causal inference when the actors are connected via an underlying network structure. Our key contribution is a model for causality when the underlying network is endogenous; where the ties between actors and the actor covariates are statistically dependent. We develop a joint model for the relational and covariate generating process that avoids restrictive separability and fixed network assumptions, as these rarely hold in realistic social settings. While our framework can be used with general models, we develop the highly expressive class of Exponential-family Random Network models (ERNM) of which Markov random fields and Exponential-family Random Graph models are special cases. We present potential outcome-based inference within a Bayesian framework and propose a modification to the exchange algorithm to allow for sampling from ERNM posteriors. We present results of a simulation study demonstrating the validity of the approach. Finally, we demonstrate the value of the framework in a case study of smoking in the context of adolescent friendship networks.
在网络环境中进行因果推断需要仔细考虑行为主体结果之间通常复杂的依赖性。治疗溢出或结果干扰效应尤为重要。当行为主体通过潜在的网络结构相连时，我们考虑因果推断。我们的关键贡献是一个在潜在网络为内生时的因果关系模型；即行为主体之间的联系和行为主体协变量在统计上是相关的。我们为关系生成过程和协变量生成过程开发了一个联合模型，该模型避免了限制性的可分性和固定网络假设，因为这些假设在现实社会环境中很少成立。虽然我们的框架可以与一般模型一起使用，但我们开发了具有高度表现力的指数族随机网络模型（ERNM）类，马尔可夫随机场和指数族随机图模型是其特殊情况。我们在贝叶斯框架内提出基于潜在结果的推断，并对交换算法提出一种修改，以允许从ERNM后验中进行抽样。我们给出了一个模拟研究的结果，证明了该方法的有效性。最后，我们在青少年友谊网络背景下的吸烟案例研究中展示了该框架的价值。
