Despite the potential to reduce diarrheal disease burden (Quick, et al, 2002), use of affordable point-of-use decontamination technologies such as chlorine tablets is low in many developing countries including Pakistan (0.3%, with 8% adopting any purification technology; Pakistan DHS, 2012-13). While price - even when low - can hinder adoption, free access induces only partial usage. For example, free delivery of chlorine solution in Kenya yielded a usage rate of only 34% (Dupas, et al, 2016). However, there is little evidence on ways to increase usage beyond this basic level induced by access. Akram & Mendelsohn (2017; hereafter, AM) explore an alternative hypothesis: If the households do not understand - or trust - expert opinion on benefits of usage and benefits are hard to measure, they may not be convinced of the returns to such a technology. In a pilot RCT, AM use household recordkeeping of children’s diarrhea incidence to help them learn the benefits of chlorinated drinking water (along with sharing information on the diarrhea rate from a comparable population not exposed to chlorine). Compared to a base policy of free access to chlorine tablets and expert advice on why to use them, this simple intervention increased chlorine use after one year by a remarkable 56 percentage points. It was highly cost-effective as well, with the marginal cost per DALY-averted at $495. We propose to build on this pilot by a) testing at a larger scale and b) investigating mechanisms, particularly those related to learning and social norms, to inform design choices for the next stage(s) of experimentation and eventually scale-up. The experimental design is further structured to test learning against habit formation: both processes require intense initial engagement and can yield sustained behavioral change, but economists know little about which is most effective for behavioral change (and technology adoption) broadly. Particularly in a space where returns are not obvious and existing information campaigns have largely failed, investigating which mental process is most operative regarding long term adoption of preventive health behaviors is crucial to policy design: do we, as policymakers, invest in subsidizing an activity repeatedly so people develop a habit, or do we invest in improving our information campaigns so individuals can better understand the returns to a behavior?