Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on the identification of threats to validity and the inclusion of design features that may prevent those threats from occurring or render them implausible. Rubin's approach focuses on the precise specification of both the possible outcomes for each participant and assumptions that are mathematically sufficient to estimate the causal effect. In this article, the authors compare the perspectives provided by the 2 approaches on randomized experiments, broken randomized experiments in which treatment nonadherence or attrition occurs, and observational studies in which participants are assigned to treatments on an unknown basis. The authors highlight dimensions on which the 2 approaches have different emphases, including the roles of constructs versus operations, threats to validity versus assumptions, methods of addressing threats to internal validity and violations of assumptions, direction versus magnitude of causal effects, role of measurement, and causal generalization. The authors conclude that investigators can benefit from drawing on the strengths of both approaches in designing research. (Contains 3 tables, 3 figures, and 13 footnotes.)