Mixtape-Sessions/Heterogeneous-Effects

Machine Learning and Heterogeneous Effects taught by Brigham Frandsen

SchemeSASStataJupyter NotebookPythonTeXR
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About The holy grail of causal inference is the individual-level treatment effect: how would a particular patient respond to a drug? Which users will respond most to a targeted ad? Would a given student be helped or harmed by a classroom intervention? This session introduces machine learning tools for estimating heterogeneous treatment effects like random causal forests. The course goes over the theory and concepts as well as the nitty-gritty of coding the methods up in python, R, and Stata using...
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