The second method is nearest-neighbor matching. This matches each immunized child with a control that is the un-immunized child with the nearest propensity score. Although reasonable, on occasion this leads to matches that are quite far apart in terms of propensity score. A third approach is to use radius matching, where each treatment is compared with controls whose propensity score lies within a given radius (we use a radius of 0.1) of the score of the treated child. Finally, we use a kernel estimator that allows matching over a wide radius but weights the matches, with more weight (we use a normal distribution as the kernel to generate weights) given to controls that are closer to the immunized child.