Department of Psychiatry, University of Rochester Medical Center, Rochester, New York, USA.
Department of Psychological Sciences, Texas Tech University, Lubbock, Texas, USA.
Suicide Life Threat Behav. 2021 Feb;51(1):148-161. doi: 10.1111/sltb.12681.
Although causal inference is often straightforward in experimental contexts, few research questions in suicide are amenable to experimental manipulation and randomized control. Instead, suicide prevention specialists must rely on observational data and statistical control of confounding variables to make effective causal inferences. We provide a brief summary of recent covariate practice and a tutorial on casual inference tools for covariate selection in suicide research.
We provide an introduction to modern causal inference tools, suggestions for statistical control selection, and demonstrations using simulated data.
Statistical controls are often mistakenly selected due to their significant correlation with other study variables, their consistency with previous research, or no explicit reason at all. We clarify what it means to control for a variable and when controlling for the wrong covariates systematically distorts results. We describe directed acyclic graphs (DAGs) and tools for identifying the right choice of covariates. Finally, we provide four best practices for integrating causal inference tools in future studies.
The use of causal model tools, such as DAGs, allows researchers to carefully and thoughtfully select statistical controls and avoid presenting distorted findings; however, limitations of this approach are discussed.
尽管因果推断在实验环境中通常很直接，但很少有自杀相关的研究问题可以通过实验操作和随机对照来解决。相反，自杀预防专家必须依赖观察性数据和混杂变量的统计控制来进行有效的因果推断。我们简要总结了最近的协变量实践，并就自杀研究中协变量选择的因果推断工具提供了一个教程。
我们介绍了现代因果推断工具、统计控制选择的建议，并使用模拟数据进行了演示。
由于与其他研究变量的显著相关性、与先前研究的一致性或根本没有明确的原因，统计控制往往会被错误地选择。我们澄清了控制变量的含义，以及何时错误地控制协变量会系统地扭曲结果。我们描述了有向无环图（DAGs）和用于确定正确选择协变量的工具。最后，我们提供了将因果推断工具整合到未来研究中的四项最佳实践。
使用因果模型工具，如 DAGs，允许研究人员仔细而有思考地选择统计控制，并避免呈现扭曲的发现；然而，我们也讨论了这种方法的局限性。
