Happiness/well-being researchers who use quantitative analysis often do not give persuasive reasons why particular variables should be included as controls in their cross-sectional models. One commonly sees notions of a “standard set” of controls, or the “usual suspects”, etc. These notions are not coherent and can lead to results that are significantly biased with respect to a genuine causal relationship.

This article presents some core principles for making more effective decisions of that sort. The contribution is to introduce a framework (the “causal revolution”, e.g. Pearl and Mackenzie 2018) unfamiliar to many social scientists (though well established in epidemiology) and to show how it can be put into practice for empirical analysis of causal questions. In simplified form, the core principles are: control for confounding variables, and do not control for intervening variables or colliders. A more comprehensive approach uses directed acyclic graphs (DAGs) to discern models that meet a minimum/efficient criterion for identification of causal effects.

The article demonstrates this mode of analysis via a stylized investigation of the effect of unemployment on happiness. Most researchers would include other determinants of happiness as controls for this purpose. One such determinant is income—but income is an intervening variable in the path from unemployment to happiness, and including it leads to substantial bias. Other commonly-used variables are simply unnecessary, e.g. religiosity and sex. From this perspective, identifying the effect of unemployment on happiness requires controlling only for age and education; a small (parsimonious) model is evidently preferable to a more complex one in this instance.

使用定量分析的幸福/幸福研究人员通常没有给出有说服力的理由，为什么应将特定变量作为其横截面模型的对照。人们通常会看到“标准的”控制集或“通常的可疑者”等概念。这些概念不一致，会导致结果因真正的因果关系而有明显偏差。

本文介绍了一些核心原则，可以使您做出更有效的决策。所做的贡献是引入了许多社会科学家都不熟悉的框架（“因果关系革命”，例如Pearl和Mackenzie 2018）（尽管在流行病学方面已经很成熟），并展示了如何将其付诸实践以对因果问题进行实证分析。在简化形式中，核心原则是：控制混杂变量，而不控制中间变量或对撞机。一种更全面的方法使用有向无环图（DAG）来识别满足识别因果效应的最小/有效标准的模型。

本文通过对失业对幸福的影响进行风格化研究，证明了这种分析模式。大多数研究人员会将幸福的其他决定因素作为此目的的控制因素。收入是一个这样的决定因素，但是收入是从失业到幸福的中间干预变量，包括在内会导致巨大的偏见。其他常用变量根本就没有必要，例如宗教信仰和性别。从这个角度来看，确定失业对幸福的影响只需要控制年龄和受教育程度；在这种情况下，小型（简约）模型显然比更复杂的模型更可取。