Effect size benchmarks are used as guidelines for conducting power analysis and Bayesian analysis, guiding theory, interpreting practical significance, and reviewing scientific progress. However, effect size estimates that are correlational directly violate the definition of an "effect", as they do not capture a cause-and-effect relationship. The current work begins with a review of the current state of the literature and presents a continuum of causal-inference strength. Next, to demonstrate this conceptualization, a comprehensive review was conducted of the leadership literature: (1) a second-order meta-analysis of leader individual differences (total k = 1,829; total N = 640,388), (2) meta-analyzed lab and field experiments (total k = 110; total N = 18,402), and (3) a narrative review of effect sizes from quasi-experimental and non-traditional experimental designs. This work concludes with implications for theory and practice, future directions for research, and methodological best practices (e.g., experimental design).