A general dynamical statistical model with causal interpretation
A general dynamical statistical model with causal interpretation
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DOI：
10.1111/j.1467-9868.2009.00703.x
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发表时间：
2009-01-01
影响因子：
5.8
通讯作者：
Gegout-Petit, Anne
中科院分区：
文献类型：
--
作者：
Commenges, Daniel;Gegout-Petit, Anne
我们开发了一个通用的动态模型作为因果解释的框架。首先，我们国家的标准的可测性的过程中所涉及的随机过程的Doob-Meyer分解的局部独立性，然后我们定义直接和间接的影响。我们提出了一个定义的因果关系的影响使用的概念的“物理系统”。这一框架使描述性和解释性统计模型之间的联系成为可能，并涵盖了定量过程和事件。本文的特点之一是明确区分了系统模型和观测模型。我们给出了人类免疫缺陷病毒载量和CD 4细胞计数的传统联合模型的动态表示。我们发现它不足以捕捉因果关系的影响，而在相反的已知机制的感染人类免疫缺陷病毒可以直接通过微分方程系统表示。
We develop a general dynamical model as a framework for causal interpretation. We first state a criterion of local independence in terms of measurability of processes that are involved in the Doob-Meyer decomposition of stochastic processes; then we define direct and indirect influence. We propose a definition of causal influence using the concepts of a 'physical system'. This framework makes it possible to link descriptive and explicative statistical models, and encompasses quantitative processes and events. One of the features of the paper is the clear distinction between the model for the system and the model for the observation. We give a dynamical representation of a conventional joint model for human immunodeficiency virus load and CD4 cell counts. We show its inadequacy to capture causal influences whereas in contrast known mechanisms of infection by the human immunodeficiency virus can be expressed directly through a system of differential equations.