Causal reasoning from longitudinal data
Causal reasoning from longitudinal data
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DOI：
10.1111/j.1467-9469.2004.02-134.x
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发表时间：
2004-06-01
影响因子：
1
通讯作者：
Parner, J
中科院分区：
文献类型：
--
作者：
Arjas, E;Parner, J
本文通过对实验或观察的纵向数据应用统计方法，回顾了在试图为因果解释和结论寻找经验支持时遇到的一些关键统计思想。在这样的数据中，通常是一组个体随着时间的推移而被跟踪，然后每个个体都记录了一系列协变量测量以及在分析中被解释为原因的控制变量的值，最后报告个体结果或反应。特别注意的是潜在的重要问题的混淆。我们提供了条件，在这些条件下，至少在原则上，可以完成对因果效应的无混淆估计。我们处理因果问题的方法完全是概率的，我们应用贝叶斯思想和技术来处理相应的统计推断。特别是，我们使用标记点过程的一般框架来建立概率模型，并考虑后验预测分布作为评估因果效应的自然汇总度量。我们还将其与该领域最近的相关工作联系起来，特别是朱迪亚·珀尔基于图形模型的公式和他所谓的概率计算。两个例子说明因果推理的不同方面进行了详细讨论。
This paper reviews some of the key statistical ideas that are encountered when trying to find empirical support to causal interpretations and conclusions, by applying statistical methods on experimental or observational longitudinal data. In such data, typically a collection of individuals are followed over time, then each one has registered a sequence of covariate measurements along with values of control variables that in the analysis are to be interpreted as causes, and finally the individual outcomes or responses are reported. Particular attention is given to the potentially important problem of confounding. We provide conditions under which, at least in principle, unconfounded estimation of the causal effects can be accomplished. Our approach for dealing with causal problems is entirely probabilistic, and we apply Bayesian ideas and techniques to deal with the corresponding statistical inference. In particular, we use the general framework of marked point processes for setting up the probability models, and consider posterior predictive distributions as providing the natural summary measures for assessing the causal effects. We also draw connections to relevant recent work in this area, notably to Judea Pearl's formulations based on graphical models and his calculus of so-called do-probabilities. Two examples illustrating different aspects of causal reasoning are discussed in detail.