Causal machine learning for development data
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.
- Novi Quadrianto
- Jeremy Reffin
- Sara Romiti (PhD Student 2018–present)
- Bradley Butcher (PhD Student 2018–present)
- Myles Bartlett (PhD Student 2018–present)
- Chris Robinson (PhD Student 2018–present)
- Oliver Thomas (PhD Student 2017–present)
- Artyom Gadetsky, Kirill Struminsky, Christopher Robinson, Novi Quadrianto and Dmitry Vetrov. Low-variance black-box gradient estimates for the Plackett-Luce distribution. Thirty-Fourth AAAI Conference on Artificial Intelligence AAAI, 2020.
- Bradley Butcher, Vincent S Huang, Jeremy Reffin, Sema K Sgaier, Grace Charles, Novi Quadrianto. Causal datasheet - An approximate guide to practically assess Bayesian networks in the real world. NeurIPS workshop on machine learning and causal inference for improved decision making, 2019.