Social scientists routinely address temporal dependence by adopting a simple technical fix. However, the correct identification strategy for a causal effect depends on causal assumptions. These need to be explicated and justified; almost no studies do so. This article addresses this shortcoming by offering a precise general statement of the (nonparametric) causal assumptions required to identify causal effects under temporal dependence. In particular, this article clarifies when one should condition or not condition on lagged dependent variables (LDVs) to identify causal effects: one should not condition on LDVs, if there is no reverse causation "and" no outcome autocausation; one should condition on LDVs if there are no unobserved common causes of treatment and the lagged outcome, "or" no unobserved persistent causes of the outcome. When only one of these is true (with one exception), the incorrect decision will induce bias. Absent a well-justified identification strategy, inferences should be appropriately qualified.