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Data Science for Dynamic Intervention Decision-making Lab


Jamie Yap

Research Associate

Institute for Social Research
University of Michigan
Having grown up and worked in an emerging market, I have a soft spot for developing and deploying innovative and cost-effective technology in challenging and ambiguous environments. In this context, the promise of interventions that adapt over time to the person and their environment is balanced by the need to collect good quality data and deliver interventions under very tight budgetary and even logistical constraints. Sequential experiments can be used to collect good quality data to enable causal inference when interventions are not static one-off events, but rather change over time, and when outcomes can be short or long term. They enable the development of effective, low-cost, and feasible interventions — effective adaptive interventions that can scale in this context.
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