Statisticians and social and computer scientists tend to approach causality and causal inference with particular theories of causality in mind, and defend tools that are supposed to support causal claims from the point of view of that theory. This entry explains why theoretical and methodological pluralism with respect to causality can benefit causal inference. To this aim, we first discuss various understandings of the concept of causality, and of mechanisms, and emphasize that none of them can be considered as intrinsically superior to another. We then discuss typical design- and model-based identification strategies of causal effects from within the potential outcome approach, and point to the crucial role of untestable assumptions for defending causal claims within experimental and observational methods. Finally, we explain how computational tools like agent-based modeling can aid causal inference, and argue that persuasive causal claims in fact require data and arguments produced by methods that are based on different assumptions and that incorporate different views of causality and mechanisms.