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摘要翻译：
我们考虑在观察数据的基础上学习和比较动态治疗策略的后果的问题。我们在一个概率决策理论框架内阐述了这一点。我们的方法与Robins等人的相关工作进行了比较：特别是，我们展示了Robins的“G计算”算法是如何从这个决策理论的角度自然产生的。仔细注意了证明使用这个公式是合理的所需的数学和实质性条件。这些条件围绕着一个我们称之为稳定性的性质，它关系到观察和干预制度的概率行为。我们展示了一个序列随机化的假设（或者没有不可测量的混杂物），或者一个序列无关性的替代假设，是如何被用来推断稳定性的。概率影响图被用来简化操作，并讨论了它们的力量和局限性。我们将我们的方法与基于因果DAG或潜在反应模型的替代公式进行了比较。我们的目的是表明，将评估动态治疗策略的问题表述为决策分析问题带来了清晰、简单和一般性。
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英文标题：
《Identifying the consequences of dynamic treatment strategies: A
decision-theoretic overview》
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作者：
A. Philip Dawid and Vanessa Didelez
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最新提交年份：
2010
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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：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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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 consider the problem of learning about and comparing the consequences of dynamic treatment strategies on the basis of observational data. We formulate this within a probabilistic decision-theoretic framework. Our approach is compared with related work by Robins and others: in particular, we show how Robins's 'G-computation' algorithm arises naturally from this decision-theoretic perspective. Careful attention is paid to the mathematical and substantive conditions required to justify the use of this formula. These conditions revolve around a property we term stability, which relates the probabilistic behaviours of observational and interventional regimes. We show how an assumption of 'sequential randomization' (or 'no unmeasured confounders'), or an alternative assumption of 'sequential irrelevance', can be used to infer stability. Probabilistic influence diagrams are used to simplify manipulations, and their power and limitations are discussed. We compare our approach with alternative formulations based on causal DAGs or potential response models. We aim to show that formulating the problem of assessing dynamic treatment strategies as a problem of decision analysis brings clarity, simplicity and generality.
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PDF链接：
https://arxiv.org/pdf/1010.3425
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