Chapter 1 starts by embedding the methods presented in this monograph into the rich landscape of statistical methods for causality research. Specifically, it starts with contrasting methods of causal inference and methods of causal structure learning (also known as causal discovery). While the former class of statistical methods can be considered well established across the developmental, psychological, and social sciences, the latter class only recently received attention. The methods of direction of dependence presented here can be characterized as a confirmatory approach to probe hypothesized causal structures of variable relations. To introduce the reader to the line of thinking that is involved when using methods of direction of dependence, prototypical research questions are presented that can be answered with the presented statistical tools and application areas that can benefit from taking a direction of dependence perspective in the analysis of research data are outlined. The methods of direction of dependence rely on higher moments of variables to discern causal structures from observational data. Thus, the chapter closes with an introductory discussion of moments of variables.