Chapter 11 starts with embedding the framework of Direction Dependence Analysis (DDA) into the methodological landscape of approaches to establishing causal effects. Specifically, it discusses the role of DDA relative to randomized experiments and describes three application scenarios in which DDA provides novel insights into the causal mechanisms underlying variable relations. It then discusses the use of statistical principles of DDA that emerge from incorporating non-Gaussianity information in existing methods for causal inference such as instrumental variable estimation, cross-lagged panel modeling, Granger-causality testing, and estimating models of reciprocal causation. For each approach, we outline how and to what extent principles of DDA can aid the pursuit of consistently estimating causal effects. The chapter closes with potential extensions of DDA principles to the categorical data domain and a reminder of the importance of data quality when applying DDA in practice. Here, it focuses on potential adverse effects of measurement error, the role of psychometric measurement of constructs of interest, and other forms of data contamination that can bias DDA decisions (e.g., ceiling/floor effects). Overall, however, we conclude that DDA and related methods that use non-Gaussian information for studying causation constitute an exciting and developing field of methods research.