路径依赖结构方程模型
Path Dependent Structural Equation Models
摘要：
纵向数据的因果分析通常假设与变量相关的定性因果结构随时间保持不变。在以离散时间步长在性质上不同的状态之间转换的结构化系统中，这种方法在两个方面存在缺陷。首先，时变变量可能具有需要捕获的特定于状态的因果关系。其次，干预可能导致干预下游的状态转换不同于数据中实际观察到的状态转换。换句话说，干预可能会违背事实地改变系统随后的时间演变。我们介绍了因果图模型的推广，路径依赖结构方程模型（PDSEMs），可以描述这样的系统。我们将展示如何在这样的模型中进行因果推断，并说明其在模拟中的使用和从鼻中隔成形术手术过程中获得的数据。
英文摘要：
Causal analyses of longitudinal data generally assume that the qualitative causal structure relating variables remains invariant over time. In structured systems that transition between qualitatively different states in discrete time steps, such an approach is deficient on two fronts. First, time-varying variables may have state-specific causal relationships that need to be captured. Second, an intervention can result in state transitions downstream of the intervention different from those actually observed in the data. In other words, interventions may counterfactually alter the subsequent temporal evolution of the system. We introduce a generalization of causal graphical models, Path Dependent Structural Equation Models (PDSEMs), that can describe such systems. We show how causal inference may be performed in such models and illustrate its use in simulations and data obtained from a septoplasty surgical procedure.
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