Causal inference in suicide research: When you should (and should not!) control for extraneous variables.

Abstract

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.