STATS 361: Causal Inference

STATS 361 (also previously offered as OIT 661) is a graduate level class in causal inference, with a focus on topics including randomized and observational studies, doubly robust estimation, instrumental variables, graphical modeling, dynamic policies, etc. The class is targeted at PhD students who have already completed first-year coursework on the theory of statistics. Compiled notes from the Spring 2022 version of the class are available here.

I usually teach this class every other year. For current scheduling and information, see explorecourses.

ECON 293 / MGTECON 634: Machine Learning and Causal Inference

The aim of this class, co-taught with Susan Athey, is to get applied researchers in social sciences up to speed on recent developments in methods for causal inference, with a focus on machine learning. The class is project-focused, and instruction is built around a mix of lectures and software tutorials. Some of the lectures and all software tutorials are available online.

For current scheduling and information, see explorecourses.

OIT 276: Data and Decisions - Accelerated

Accelerated Data and Decisions is a first-year MBA course in statistics and regression analysis. The course is taught using a flipped classroom model that combines extensive online materials with a more lab-based classroom approach. Traditional lecture content are learned through online videos, simulations, and exercises, while time spent in the classroom will be discussions, problem solving, or computer lab sessions.

For current scheduling and information, see explorecourses.

OIT 644: Research in Operations, Information and Technology

This is a year-long recurring research seminar for PhD students in Operations, Information and Technology. For current scheduling and information, see explorecourses.