Although review papers on causal inference methods are now available, there is a lack of introductory overviews on what they can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline (“point exposure”) and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score‐based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a “simulation learner,” that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on www.ofcaus.org, where SAS and Stata code for analysis is also provided.

中文翻译：

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制定因果问题和原则性统计答案。


尽管现在已有关于因果推理方法的综述论文，但缺乏对它们可以提供什么以及选择一种特定方法的指导标准的介绍性概述。本教程概述了将感兴趣的暴露设置在选定的基线（“点暴露”）并且目标结果在稍后的时间点出现的情况。我们首先提出相关的因果问题，并具体说明所涉及的可能的暴露水平以及与该问题相关的人群。使用潜在结果框架，我们描述了因果效应和估计方法的原则性定义，这些估计方法根据是否调用不可测量的混杂假设（包括结果回归和基于倾向评分的方法）或带有附加假设的工具变量进行分类。我们主要关注连续结果和因果平均治疗效果。我们讨论解释、挑战和潜在的陷阱，并使用“模拟学习器”来说明应用，该模拟学习器模仿各种母乳喂养干预措施对儿童后期发育的影响。这涉及一个典型的模拟组件，其中包含受随机干预研究启发而生成的暴露、协变量和结果数据。模拟学习器进一步生成各种（链接的）暴露类型，每个观察单元具有一组可能的值，从中生成观察到的以及潜在的结果数据。因此，它提供了几种因果效应的真实值。 www.ofcaus.org 上提供了用于数据生成和分析的 R 代码，其中还提供了用于分析的 SAS 和 Stata 代码。