Interrupted time-series (ITS) are often used to assess the causal effect of a planned or even unplanned shock introduced into an on-going process. The pre-intervention slope is supposed to index the causal counterfactual, and deviations from it in mean, slope or variance are used to indicate an effect. However, a secure causal inference is only warranted if: (1) The intervention is exogenous and not a product of prior time series values; (2) the intervention is not correlated with some other force that abruptly affects the outcome at the same time as the intervention; (3) onset of the intervention is abrupt or its dissemination is otherwise well described; (4) the response occurs abruptly or with a theoretically known delay; and (5) correlated error is controlled so that the standard error of any effect is unbiased. It also helps if (6) the effect is large relative to the size of the inter-temporal variation prior to the intervention. Although this is a long series of contingencies, there are nonetheless many examples of interrupted time-series that meet these conditions. Some of the examples presented to date require no statistical analysis; their inter-occular impact is striking because the effect is so specific to the intervention time point and so large relative to the prior inter-temporal variation. This paper argues that abbreviated interrupted time series can help with causal identification even with as few as three pre-intervention time points provided that some form of a non-equivalent comparison series is available. It illustrates the range and quality of non-equivalent time series comparisons. It also illustrates how one kind of comparison time series helps identify the short-term effect of No Child Left Behind on academic achievement. The present proposal tilts towards the last purpose since elaborating the NCLB example allows the other purposes to be explored at the same time. (Contains 1 table and 4 figures.)