Causal machine learning for development data

Funded by Surgo Foundation (15 April 2017–present)


Surgo Foundation is launching the Surgo Machine Learning Initiative for Precision Public Health to explore the feasibility of applying causal machine learning methods to international development data. Surgo has formed a strong and diverse consortium of partners across the private and non-profit sectors including the Bill and Melinda Gates Foundation (BMGF), GNS Healthcare, the University of Manitoba, and the University of Sussex.

In its first proof-of-concept project, ML4PxP will begin by testing several potential causal machine learning approaches on reproductive, maternal, and child health data sets from Uttar Pradesh, India. Together, the consortium is innovating to determine whether and how such models can be applied to help solve big international development questions.

Principal Investigator(s)

Team Members


More projects