Abstract

Statistical network models based on Pairwise Markov Random Fields (PMRFs) are popular tools for analyzing multivariate psychological data, in large part due to their perceived role in generating insights into causal relationships: a practice known as causal discovery in the causal modeling literature. However, since network models are not presented as causal discovery tools, the role they play in generating causal insights is poorly understood among empirical researchers. In this paper, we provide a treatment of how PMRFs such as the Gaussian Graphical Model (GGM) work as causal discovery tools, using Directed Acyclic Graphs (DAGs) and Structural Equation Models (SEMs) as causal models. We describe the key assumptions needed for causal discovery and show the equivalence class of causal models that networks identify from data. We clarify four common misconceptions found in the empirical literature relating to networks as causal skeletons; chains of relationships; collider bias; and cyclic causal models.

摘要

基于成对马尔可夫随机场 (PMRF) 的统计网络模型是分析多元心理数据的流行工具，这在很大程度上是由于它们在产生对因果关系的洞察力方面的作用：因果建模文献中称为因果发现的实践。然而，由于网络模型并未作为因果发现工具呈现，因此实证研究人员对它们在产生因果洞察力方面所起的作用知之甚少。在本文中，我们使用有向无环图 (DAG) 和结构方程模型 (SEM) 作为因果模型，介绍了 PMRF（例如高斯图形模型 (GGM)）如何作为因果发现工具工作。我们描述了因果发现所需的关键假设，并展示了网络从数据中识别的因果模型的等价类。我们澄清了在将网络作为因果骨架的实证文献中发现的四种常见误解；关系链；对撞机偏差；和循环因果模型。