Computational agent-based models are entering the toolbox of quantitative sociologists. However, markedly contrasting views still exist as to its capacity to contribute to causally-oriented empirical research. Building on selected works across disciplines ranging from computer science to philosophy, we connect scholarship on causality, mechanisms, and simulation methods, and provide the first systematic discussion on how, if at all, computational agent-based models warrant causal inference. First, we argue that this method can produce causally-relevant evidence when (and only when) specific conditions are met. Then, we show that data-driven methods for causal inference face analogous challenges. Finally, upon endorsing a pragmatist view of evidence, we defend an approach to causal analysis that combines evidence from agent-based modeling and data-driven methods. This evidential variety lends credibility to causal inference in virtue of drawing on complementary , and equally important, kinds of evidence.