Workshop description:
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 real-world examples. This course can be taken as a follow-up to the Machine Learning and Causal Inference mixtape session, or as a stand-alone course.
This is one of our advanced courses. These courses are designed assuming a solid foundation in the basics of machine learning and causal inference and will cover the frontiers of the topic. A good review is the intro course: https://github.com/Mixtape-Sessions/Machine-Learning
About the instructor:
Brigham Frandsen is an associate professor at Brigham Young University after completing his Ph.D. in Economics at MIT, where his dissertation focused on econometric methodology and labor economics. After his Ph.D., Dr. Frandsen was selected as a Robert Wood Johnson Scholar in Health Policy Research at Harvard University where he spent two years in residence furthering his research in econometrics and labor economics, as well as adding health policy to his research agenda. Dr. Frandsen's methodological research focuses on causal inference on distributional effects. He applies these methodologies to questions about the impact of labor market institutions and interventions on education and earnings outcomes. His health policy research deals with the consequences of fragmentation in the U.S. health care system. In addition to research, Dr. Frandsen enjoys hiking and mountain biking with his wife, Christine, and their four children.
International and Student Pricing:
Email causalinf@mixtape.consulting for student and international pricing.
