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Causal Inference & Targeted Machine Learning



Full course description

Level: Intermediate & Advanced

Intermediate courses focus on applying a new analytic tool or framework within the topic area. Advanced courses are similar to intermediate courses but require completing a more complex project or assignment. 

Track: Analyst track

Courses targeted to analysts focus learning new approaches for managing and analyzing data in statistical software such as R or SAS

Intended Audience:

This course is for persons who want to learn more about the principles and practices of estimating cause and effects with messy real-world data. Participants should be familiar with basic probability theory and experience conducting multivariable regression analyses (i.e., generalized linear models).   


This course will introduce the Causal Roadmap, a general framework for Causal Inference. The statistical methods include G-computation, inverse probability weighting (IPW), and targeted minimum loss-based estimation (TMLE) with Super Learner, an ensemble machine learning method. The emphasis will be on practical implementation and real-world challenges and solutions. This is an enhanced iteration of the causal inference course that was offered from July to September 2023.  

Time Commitment: 10 hours of lectures, R/SAS practice, and assignments for advanced option, 2-3 hours/week of lectures and guided practice videos for intermediate option.


Certificates are not currently available for this course.

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