This paper introduces causal scoring as a novel approach to frame causal estimation in the context of decision making. Causal scoring entails the estimation of scores that support decision making by providing insights into causal effects. We present three valuable causal interpretations of these scores: effect estimation (EE), effect ordering (EO), and effect classification (EC). In the EE interpretation, the causal score represents the effect itself. The EO interpretation implies that the score can serve as a proxy for the magnitude of the effect, enabling the sorting of individuals based on their causal effects. The EC interpretation enables the classification of individuals into high- and low-effect categories using a predefined threshold. We demonstrate the value of these alternative causal interpretations (EO and EC) through two key results. First, we show that aligning the statistical modeling with the desired causal interpretation improves the accuracy of causal estimation. Second, we establish that more flexible causal interpretations are plausible in a wider range of data-generating processes and propose conditions to assess their validity. We showcase the practical utility of the causal scoring framework through examples in diverse fields such as advertising, healthcare, and education, illustrating how it facilitates reasoning about flexible causal interpretations of statistical estimates in various contexts. The examples encompass confounded estimates, effect estimates on surrogate outcomes, and even predictions about non-causal quantities as potential causal scores.

翻译：本文提出因果评分（causal scoring）作为面向决策情境的因果估计新框架。该框架通过估计支持决策的评分，揭示因果效应信息。我们给出这些评分的三种因果解释：效应估计（EE）、效应排序（EO）与效应分类（EC）。在EE解释中，因果评分直接表征效应量值；EO解释表明评分可作为效应大小的代理指标，实现个体按因果效应排序；EC解释则通过预设阈值将个体划分为高/低效应类别。本研究通过两项关键结果论证替代性因果解释（EO与EC）的实践价值：首先证明将统计建模与目标因果解释对齐可提升因果估计精度；其次建立数据生成过程更灵活时替代性解释的适用条件，并提出有效性验证准则。我们通过广告、医疗与教育等多领域实例展示因果评分框架的实用价值，揭示该框架如何促进不同情境下统计估计量的灵活因果解释。示例涵盖混杂估计、替代结局上的效应估计，乃至将非因果量预测作为潜在因果评分的应用场景。