数据科学中的因果关系
A Primer on Causality in Data Science
摘要：
数据科学中的许多问题从根本上讲都是因果关系，因为我们的目标是了解某种暴露（随机或非随机）对结果利益的影响。即使是看似无因果关系的研究，例如以预测或流行率估计为目标的研究，也有因果因素，包括差别审查或测量。因此，作为数据科学家，我们需要考虑产生数据的潜在因果机制，而不仅仅是在这些数据中观察到的模式或关联。在这项工作中，我们回顾了Petersen和van der Laan（2014）的“因果路线图”，以介绍因果推断中的一些关键概念。与其他因果框架类似，路线图的步骤包括明确说明科学问题，定义因果模型，将科学问题转化为因果参数，评估将因果参数表示为统计被估量所需的假设，实施统计估计，包括参数和半参数方法，以及解释我们的发现。我们相信，在数据科学中使用这样的框架将有助于确保我们的统计分析受到推动我们研究的科学问题的指导，同时避免过度解释我们的结果。我们专注于在单个时间点发生的暴露的影响，并强调使用有针对性的极大似然估计（TMLE）与超级学习者。
英文摘要：
Many questions in Data Science are fundamentally causal in that our objective is to learn the effect of some exposure, randomized or not, on an outcome interest. Even studies that are seemingly non-causal, such as those with the goal of prediction or prevalence estimation, have causal elements, including differential censoring or measurement. As a result, we, as Data Scientists, need to consider the underlying causal mechanisms that gave rise to the data, rather than simply the pattern or association observed in those data. In this work, we review the 'Causal Roadmap' of Petersen and van der Laan (2014) to provide an introduction to some key concepts in causal inference. Similar to other causal frameworks, the steps of the Roadmap include clearly stating the scientific question, defining of the causal model, translating the scientific question into a causal parameter, assessing the assumptions needed to express the causal parameter as a statistical estimand, implementation of statistical estimators including parametric and semi-parametric methods, and interpretation of our findings. We believe that using such a framework in Data Science will help to ensure that our statistical analyses are guided by the scientific question driving our research, while avoiding over-interpreting our results. We focus on the effect of an exposure occurring at a single time point and highlight the use of targeted maximum likelihood estimation (TMLE) with Super Learner.
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