Darren Colby
Darren Colby is an MS candidate in computational analysis and public policy at the University of Chicago. His public policy focus area is international security and development and he is motivated by the prospect of using machine learning and statistics to answer important questions and solve challenges in international security. He also holds an AB in government from Dartmouth College and previously served as an information systems specialist in the US Army for six years.
Session
Causal inference has proven invaluable to many organizations, however, it is often infeasible because it requires them to conduct expensive experiments. Fortunately, advances in causal machine learning give us new methods to estimate causal effects from observational data. Accordingly, in this talk, I will introduce CausalELM, a package that implements causal estimators based on extreme learning machines, and discuss how it can be used to estimate average and individualized treatment effects.