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摘要翻译：
我们发展了一个通用的动力学模型作为可能的因果解释的框架。我们首先根据随机过程的Doob-Meyer分解所涉及的过程的可测性提出了局部独立性的判据，如Aalen(1987)；然后我们定义了直接影响和间接影响。我们用物理系统的概念提出了因果影响的定义。这一框架使联系描述性和解释性统计模型成为可能，并包括定量过程和事件。本文的特点之一是对系统模型和观测模型的明确区分。我们给出了HIV载量和CD4计数的常规联合模型的动力学表示。我们表明它不足以捕捉因果影响，相反，已知的HIV感染机制可以直接通过微分方程系统来表达。
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英文标题：
《A general dynamical statistical model with possible causal
interpretation》
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作者：
Daniel Commenges and Anne Gegout-Petit
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最新提交年份：
2007
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分类信息：
一级分类：Mathematics 数学
二级分类：Statistics Theory 统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics 统计学
二级分类：Statistics Theory 统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
We develop a general dynamical model as a framework for possible causal interpretation. We first state a criterion of local independence in terms of measurability of processes involved in the Doob-Meyer decomposition of stochastic processes, as in Aalen (1987); then we define direct and indirect influence. We propose a definition of causal influence using the concepts of ``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 this 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 HIV load and CD4 counts. We show its inadequacy to capture causal influences while on the contrary known mechanisms of HIV infection can be expressed directly through a system of differential equations.
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PDF链接：
https://arxiv.org/pdf/710.4396
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