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Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Nick Huntington-Klein, Ph.D.

This course offers an overview of difference-in-differences (DID) methodology. DID compares before/after differences for a treated group against before/after differences for a group that did not receive treatment at that time to estimate a causal effect of treatment.

Difference-in-differences can be applied in many settings, and is probably the most-used quasi-experimental design in the modern quantitative social sciences. Learning to use and evaluate DID designs is crucial for policy evaluation and understanding the applied causal inference literature. However, using DID appropriately can be tricky, and several poor practices have become common in the literature (especially in regards to rollout designs where different groups receive treatment at different times).

In this course, we will address the fundamentals of difference-in-differences methods in depth, with special attention to the many details of execution. We will also evaluate several studies so that participants will be able to understand and use findings from the published literature that uses DID methods, such as work on minimum wage or immigration.

After this course, you will be able to:

• Understand difference-in-differences designs and when to use them.
• Estimate DID models.
• Evaluate the plausibility of DID assumptions.
• Use popular extensions to DID, such as rollout designs.
• Understand related methods like synthetic control and matrix completion.