Course Detail

Causal Machine Learning (41917)

Course Description by Faculty

  • Farrell, Max Misra, Sanjog
  • Content
    This course will bring students to the cutting edge in causal inference, giving them a solid theoretical understanding and ready-to-deploy tools for research. Using machine learning for estimation and inference of treatment effects has become an important part of modern academic economics. Students in this class will learn the theoretical underpinnings of this material as well as how to carefully and correctly apply the techniques in research. The course will prepare students for both theoretical and applied dissertation research. Each topic will be covered for two weeks, one covering theory and one covering application. Topics will include the basics of causal inference, nonparametric estimation, semiparametric inference, and double machine learning.
  • Prerequisites
    • PhD - students only

  • Syllabus
  • Autumn 2023Section: 41917-50M 1:30PM-4:30PMLocation: TBDRemote-Only
Description and/or course criteria last updated: June 2 2023