We evaluate a large-scale set of interventions to increase demand for immunization in Haryana, India . The policies under consideration include the two most frequently discussed tools--reminders and incentives--as well as an intervention inspired by the networks literature . We cross-randomize whether (a) individuals receive SMS reminders about upcoming vaccination drives; (b) individuals receive incentives for vaccinating their children; (c) influential individuals (information hubs, trusted individuals, or both) are asked to act as"ambassadors"receiving regular reminders to spread the word about immunization in their community . By taking into account different versions (or"dosages") of each intervention, we obtain 75 unique policy combinations . We develop a new statistical technique--a smart pooling and pruning procedure--for finding a best policy from a large set, which also determines which policies are effective and the effect of the best policy . We proceed in two steps . First, we use a LASSO technique to collapse the data: we pool dosages of the same treatment if the data cannot reject that they had the same impact, and prune policies deemed ineffective . Second, using the remaining (pooled) policies, we estimate the effect of the best policy, accounting for the winner's curse . The key outcomes are (i) the number of measles immunizations and (ii) the number of immunizations per dollar spent . The policy that has the largest impact (information hubs, SMS reminders, incentives that increase with each immunization) increases the number of immunizations by 44% relative to the status quo . The most cost-effective policy (information hubs, SMS reminders, no incentives) increases the number of immunizations per dollar by 9.1 %.