Researchers using randomized controlled trials (RCTs) often subgroup or condition on auxiliary variables that are not the randomized treatment variable. There are many good reasons to condition on auxiliary variables—also referred to as control variables or covariates—in randomized experiments. In particular, designing and conducting RCTs is costly to researchers and subjects, and therefore it is important to derive greater value from RCT data; measuring not just the average treatment effect (ATE), but also finding more nuanced insights about the underlying theoretical mechanisms and generalizing the inferences. Unfortunately, there are many confusing and even contradictory guidelines on the use of subgroups or auxiliary variables in RCTs. We show how researchers can leverage covariates without biasing their causal inferences, by applying a few simple rules based on Judea Pearl’s causal diagramming framework. We demonstrate how to create a causal schema, through careful and deliberate operationalization of auxiliary covariates, in order to analyze the intermediate effects along a causal chain from the treatment to outcome; and we discuss some other ways to leverage covariates for theory development and generalization of findings from RCTs. We present a criterion for distinguishing pre-treatment and post-treatment variables that is based on directed acyclic graphs (DAGs). We provide a succinct set of guidelines to help readers begin to employ some essential techniques of DAG-based causal analysis. Finally, we provide a series of short tutorials (with accompanying simulated data and R scripts) to help readers explore the connections between RCT and observational contexts in causal diagramming. This commentary aims to raise awareness of the DAG methodology, explain its usefulness to experimental research, and encourage adoption in the IS community for studies using RCTs as well as observational data.

中文翻译：

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超越整体治疗效果：在因果结构指导下的随机实验中利用协变量

使用随机对照试验（RCT）的研究人员通常将辅助变量分组或确定条件，这些辅助变量不是随机的治疗变量。在随机实验中，有许多充分的理由来考虑辅助变量（也称为控制变量或协变量）的条件。特别是，RCT的设计和实施对研究人员和受试者而言都是昂贵的，因此从RCT数据中获得更大的价值非常重要；不仅可以测量平均治疗效果（ATE），还可以找到有关潜在理论机制的更细微的见解并推论得出的结论。不幸的是，关于在RCT中使用子组或辅助变量有许多令人困惑甚至相互矛盾的准则。我们展示了研究人员如何利用协变量而不偏向因果推理，通过基于Judea Pearl的因果图表框架应用一些简单的规则。我们演示了如何通过对辅助协变量的仔细和有意的运算来创建因果模式，以便分析从治疗到结果的因果链上的中间影响；我们讨论了利用协变量进行RCT的理论发展和结果概括的其他方法。我们提出了一种基于有向无环图（DAG）区分治疗前和治疗后变量的标准。我们提供了一组简洁的指南，以帮助读者开始使用基于DAG的因果分析的一些必不可少的技术。最后，我们提供了一系列简短的教程（以及随附的模拟数据和R脚本），以帮助读者探索因果关系图中RCT与观测环境之间的联系。这篇评论旨在提高人们对DAG方法学的认识，解释其对实验研究的有用性，并鼓励IS社区采用RCT和观测数据进行研究。