Matching allows us to estimate the effect of a chosen variable, providing highly interpretable inference without parametric assumptions. When matching, finding good controls is where nearly all the difficulty lies. We develop a theoretical framework and a methodology to generate a set of matches, evaluate them and select a best match given the input variables. We apply this method to a problem of interest, urban data in Philadelphia. In this setting, we also outline our full data collection pipeline in order to encourage replication. In a separate time series setting, we propose a latent model in order to generate probabilities at each time point; these form the basis of an interrupted time series match.