2015-2016 2016-2017 2017-2018 2018-2019 2019-2020
Browse
by subject...
    Schedule
view...
 

1 - 1 of 1 results for: ECON 293: Machine Learning and Causal Inference

ECON 293: Machine Learning and Causal Inference

This course will cover statistical methods based on the machine learning literature that can be used for causal inference. In economics and the social sciences more broadly, empirical analyses typically estimate the effects of counterfactual policies, such as the effect of implementing a government policy, changing a price, showing advertisements, or introducing new products. This course will review when and how machine learning methods can be used for causal inference, and it will also review recent modifications and extensions to standard methods to adapt them to causal inference and provide statistical theory for hypothesis testing. We consider causal inference methods based on randomized experiments as well as observational studies, including methods such as instrumental variables and those based on longitudinal data. We consider the estimation of average treatment effects as well as personalized policies. Lectures will focus on theoretical developments, while classwork will consist primarily of empirical applications of the methods. Prerequisites: graduate level coursework in at least one of statistics, econometrics, or machine learning. Students without prior exposure to causal inference will need to do additional background reading in the early weeks of the course.
Terms: Spr | Units: 3
Filter Results:
term offered
updating results...
number of units
updating results...
time offered
updating results...
days
updating results...
UG Requirements (GERs)
updating results...
component
updating results...
career
updating results...
© Stanford University | Terms of Use | Copyright Complaints