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Decision-theoretic foundations for statistical causality
Journal of Causal Inference ( IF 1.4 ) Pub Date : 2021-01-01 , DOI: 10.1515/jci-2020-0008
Philip Dawid 1
Affiliation  

We develop a mathematical and interpretative foundation for the enterprise of decision-theoretic (DT) statistical causality, which is a straightforward way of representing and addressing causal questions. DT reframes causal inference as “assisted decision-making” and aims to understand when, and how, I can make use of external data, typically observational, to help me solve a decision problem by taking advantage of assumed relationships between the data and my problem. The relationships embodied in any representation of a causal problem require deeper justification, which is necessarily context-dependent. Here we clarify the considerations needed to support applications of the DT methodology. Exchangeability considerations are used to structure the required relationships, and a distinction drawn between intention to treat and intervention to treat forms the basis for the enabling condition of “ignorability.” We also show how the DT perspective unifies and sheds light on other popular formalisations of statistical causality, including potential responses and directed acyclic graphs.

中文翻译:

统计因果关系的决策理论基础

我们为决策理论(DT)统计因果关系的企业建立了数学和解释基础,这是表示和解决因果关系问题的直接方法。DT将因果推理重构为“辅助决策”,旨在理解何时以及如何利用外部数据(通常是观察性数据),通过利用数据与问题之间的假定关系来帮助我解决决策问题。 。因果问题的任何表示形式所体现的关系都需要更深层次的辩护,而辩护必定是与上下文相关的。在这里,我们阐明了支持DT方法应用的必要考虑因素。可交换性考虑因素用于构建所需的关系,治疗意图与治疗干预之间的区别构成了“可忽略性”促成条件的基础。我们还展示了DT视角如何统一并阐明了统计因果关系的其他流行形式,包括潜在的响应和有向无环图。
更新日期:2021-01-01
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