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Policymakers often choose a policy bundle that is a combination of differentinterventions in different dosages. We develop a new technique -- treatmentvariant aggregation (TVA) -- to select a policy from a large factorial design.TVA pools together policy variants that are not meaningfully different andprunes those deemed ineffective. This allows us to restrict attention toaggregated policy variants, consistently estimate their effects on the outcome,and estimate the best policy effect adjusting for the winner's curse. We applyTVA to a large randomized controlled trial that tests interventions tostimulate demand for immunization in Haryana, India. The policies underconsideration include reminders, incentives, and local ambassadors forcommunity mobilization. Cross-randomizing these interventions, with differentdosages or types of each intervention, yields 75 combinations. The policy withthe largest impact (which combines incentives, ambassadors who are informationhubs, and reminders) increases the number of immunizations by 44% relative tothe status quo. The most cost-effective policy (information hubs, ambassadors,and SMS reminders but no incentives) increases the number of immunizations perdollar by 9.1% relative to status quo.

Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization