Instructor: Professor Andrey Fradkin
Office: Questrom 617, Zoom
Office hours:
YOU MUST BOOK BEFOREHAND AT THIS LINK: https://calendly.com/afradkin/15min
Instructor Email: [email protected], but please try Slack first.
Zoom link for Prof. Fradkin’s office hours: https://bostonu.zoom.us/my/afradkin
TA Office Hours:
Oriol Ripalta I Maso ([email protected])
OH: Fri 3pm - 4:30pm
Chandrahas Aroori ([email protected])
OH: Wed 2pm - 3:3-pm
Meeting Times:
Tue, Thursday
8:00am - 10:45am (Morning Section)
12:30pm to 3:15pm (Afternoon Section)
In-Person Room:
HAR 312 (Afternoon)
HAR 224 (Morning)
See schedule schedule for guest speaker locations and exam location.
Key Links:
Slack (for course discussion), please enter the channel ba830_2025.
Gradescope (for submitting assignments):
When is making a change to a price, algorithm, or product worthwhile? Rather than relying on the gut intuition of a manager, businesses are increasingly using experiments and other forms of causal data analysis to answer these questions. In this class, we will learn about causal methods, when they work, how to implement them in Python, and how to apply them to topics including pricing, reputation system design, social networks, advertising, and algorithms.
An enthusiasm for learning and using code to analyze data and a willingness to do some math.
A note on technical difficulty: The purpose of this class is to expose you to causal methods and how they can be applied to practical problems. Mathematical notation is very useful in precisely stating what assumptions these analyses make. That said, this class will not focus on the technical details of these statistical procedures. This means that if you’re interested in becoming a data scientist, I would encourage you to supplement the material in this class with appropriate classes in statistics, econometrics, and computer science.
At the end of the course, students should be able to: