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 settings and propose conditions to assess their validity. We showcase the practical utility of causal scoring through diverse scenarios, including situations involving unobserved confounding due to self-selection, lack of data on the primary outcome of interest, or lack of data on how individuals behave when intervened. These examples illustrate how causal scoring facilitates reasoning about flexible causal interpretations of statistical estimates in various contexts. They encompass confounded estimates, effect estimates on surrogate outcomes, and even predictions about non-causal quantities as potential causal scores.

翻译：本文提出因果评分作为一种新颖方法，将因果估计置于决策制定的背景下进行框架构建。因果评分通过估计支持决策制定的分数，揭示因果效应的洞见。我们提出了这些分数的三种有价值的因果解释：效应估计（EE）、效应排序（EO）和效应分类（EC）。在EE解释中，因果评分直接代表效应本身；EO解释表明该分数可作为效应大小的代理，从而基于个体因果效应对其进行排序；EC解释则允许使用预定义阈值将个体分为高效应和低效应类别。我们通过两个关键结果论证了这些替代因果解释（EO和EC）的价值。首先，我们证明将统计建模与预期的因果解释对齐可提高因果估计的准确性。其次，我们确定更灵活的因果解释在更广泛的场景中具有可行性，并提出了评估其有效性的条件。通过多种场景（包括因自我选择导致的未观测混淆、缺乏目标主要结果数据、或缺乏个体受干预时行为数据等情况），展示了因果评分的实用价值。这些实例说明因果评分如何促进在不同背景下对统计估计进行灵活因果解释的推理，涉及混淆估计、替代结果的效应估计，甚至将非因果量的预测作为潜在因果评分等情形。