Abstract Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process. Especially for (maybe big) observational data, qualitative assumptions are important for the conclusions drawn and interpretation of the quantitative results. Concepts of causal inference can also help to overcome the mantra “Correlation does not imply Causation.” To motivate and introduce causal inference in introductory statistics or data science courses, we use simulated data and simple linear regression to show the effects of confounding and when one should or should not adjust for covariables.

中文翻译：

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为什么我们应该教因果推论：模拟数据的线性回归示例

摘要因果推理概念的基础知识可以帮助学生超越数据思考，即更清晰地思考数据生成过程。特别是对于（可能是很大的）观测数据，定性假设对于得出的结论和定量结果的解释很重要。因果推断的概念也可以帮助克服“相关性并不意味着因果关系”这一口头禅。为了在介绍性统计或数据科学课程中激发和引入因果推理，我们使用模拟数据和简单的线性回归来显示混淆的影响以及何时或不应该针对协变量进行调整。