This paper considers temporal depth as a conceptual framework for simplifying and reasoning about algorithmic fairness. In typical fairness applications greater temporal depth generally corresponds to stronger fairness requirements. We describe how to apply our temporal heuristics in both observational and causal probability models and their corresponding fairness definitions. As an example conclusion, one of our heuristics implies that equality of opportunity essentially justifies all disparities. In the framework of counterfactual fairness, we use temporal depth of counterfactuals to reason about common ideals like opportunity and merit, critique other causal criteria involving direct and indirect effects, and comment on long-standing debates about causation without manipulation and the use of socially constructed traits as causes. There are diverse and potentially conflicting criteria for algorithmic fairness. Heuristics like temporal depth can help us reason about fairness in a unified way, compare differing criteria, and make good decisions.