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Testing Machine learning algorithms on development data to uncover causal structures

Large-number of biological, environmental, and behavioral factors interact in complex ways to determine health outcomes. Understanding the underlying heterogeneity becomes critical to be able to target the right intervention to the right person at the right time. We are using a data-driven, machine-learning approach to 2) reveal the key environmental, biological, and behavioral features most strongly associated with differences in health (or other development) outcomes (including those that may have been previously overlooked) and 2) develop a plausible model estimating how changes to these features would change health outcomes. 

Active Dates 
01/01/2017 to 01/01/2020
Faculty Involved 
Health Topics