
Jonathan Hershaff
Senior Data Scientist
In this course, you will learn how to uncover cause-and-effect relationships in observational data—an essential skill for driving business decisions, policy evaluations, and scientific insights. You’ll explore a powerful suite of causal inference methods designed for time-series and panel data, including Interrupted Time Series, Difference-in-Differences, Event Study, Synthetic Control, and Regression Discontinuity models. Each lesson features hands-on Python exercises to build your technical fluency, and you’ll apply what you’ve learned in a final project that demonstrates your ability to estimate and validate causal effects. By the end of the course, you’ll be equipped to translate complex observational data into actionable insights that support evidence-based decision-making.

Subscription · Monthly
13 skills
9 prerequisites
Prior to enrolling, you should have the following knowledge:
You will also need to be able to communicate fluently and professionally in written and spoken English.
1 instructor
Unlike typical professors, our instructors come from Fortune 500 and Global 2000 companies and have demonstrated leadership and expertise in their professions:

Jonathan Hershaff
Senior Data Scientist
Learn causal inference with Python. Master Interrupted Time Series, DiD, Event Study & more to turn data into actionable, evidence-based insights.

Subscription · Monthly