论文标题
应用概率和统计的因果基础
The causal foundations of applied probability and statistics
论文作者
论文摘要
统计学科学（与数学统计相对）的涉及概率理论远远超出了概率理论，因为它需要现实的数据生成器的因果模型，即使是纯粹的描述目标也是如此。统计决策理论需要更多的因果关系：理性决策是为了最大程度地降低成本而在最大化收益的同时采取的行动，因此需要阐明损失和收益的原因。因此，有能力的统计实践将逻辑，上下文和概率整合到利用因果关系的叙述中的科学推论和决策中。现代统计的创始人在直觉上看到了这一现实，但在随后的统计理论中并没有得到很好的认可（而不是概率措施的因果惰性特性）。但是，统计基础和基本统计数据都可以并且应该使用正式的因果模型来教授。统计科学的因果观点符合更广泛的信息处理框架，该框架阐明并统一了常见主义者，贝叶斯和基于相关概率的统计基础。因此，因果关系理论可以看作是将计算与上下文信息联系起来的关键组成部分，而不是统计的，而是对合理的统计培训和应用必不可少的。
Statistical science (as opposed to mathematical statistics) involves far more than probability theory, for it requires realistic causal models of data generators - even for purely descriptive goals. Statistical decision theory requires more causality: Rational decisions are actions taken to minimize costs while maximizing benefits, and thus require explication of causes of loss and gain. Competent statistical practice thus integrates logic, context, and probability into scientific inference and decision using narratives filled with causality. This reality was seen and accounted for intuitively by the founders of modern statistics, but was not well recognized in the ensuing statistical theory (which focused instead on the causally inert properties of probability measures). Nonetheless, both statistical foundations and basic statistics can and should be taught using formal causal models. The causal view of statistical science fits within a broader information-processing framework which illuminates and unifies frequentist, Bayesian, and related probability-based foundations of statistics. Causality theory can thus be seen as a key component connecting computation to contextual information, not extra-statistical but instead essential for sound statistical training and applications.
