论文标题
统计因果关系的决策理论基础
Decision-theoretic foundations for statistical causality
论文作者
论文摘要
我们为决策理论统计因果关系（DT）的企业奠定了数学和解释性的基础，这是代表和解决因果问题的直接方式。 DT将因果推论重新定义为“辅助决策”，并旨在了解何时以及如何利用外部数据（通常是观察性）来帮助我通过利用数据与我的问题之间的假定关系来帮助我解决决策问题。 在因果问题的任何表示中体现的关系都需要更深入的理由，这必然与上下文有关。在这里，我们阐明了支持DT方法的应用所需的注意事项。交换性考虑因素用于构建所需的关系，并在治疗和干预以治疗“无知性”条件的基础的意图和干预措施之间提出了区别。我们还展示了DT观点如何统一并阐明其他流行的统计因果关系形式，包括潜在的反应和定向的无环图。
We develop a mathematical and interpretative foundation for the enterprise of decision-theoretic statistical causality (DT), 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.
