Difference in Differences - Online Course
A 3-Day Livestream Seminar Taught by
Gonzalo Vazquez-BareWednesday, February 3 —
Friday, February 5, 2027
10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
This seminar is part of the Causal Inference Certification, a 4-course program designed to strengthen practical skills in causal design, estimation, and interpretation. Contact us to learn how to complete the certification and access discounted pricing.
This course covers the statistical foundations and practical aspects of difference-in-differences (DiD) models. DiD models are one of the most popular tools for causal inference and policy evaluation in non-experimental settings. The main idea is to compare changes in outcomes over time between a group of units exposed to an intervention and a comparison group not exposed to that intervention. Under assumptions such as parallel trends, DiD models can identify causal effects by accounting for time-invariant unobserved differences between groups and common time trends. The use of DiD models is widespread in economics, political science, education, sociology, health sciences, environmental sciences, and many other areas.
We will introduce the potential outcomes framework to rigorously define causal effects and examine classical results and recent advances in identification, estimation, and statistical inference for DiD models.
Starting February 3, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. If you can’t join in real time, recordings will be available within 24 hours and accessible for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages, including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
After taking this workshop, you will be able to:
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- Understand the mechanics and potential pitfalls of linear regression methods for estimating treatment effects with panel/longitudinal data.
- Apply novel DiD methods to estimate treatment effects when these effects can vary both across units and over time.
- Assess the plausibility of the identification assumptions behind DiD models and conduct sensitivity analyses to evaluate the robustness of estimation results.
After taking this workshop, you will be able to:
-
- Understand the mechanics and potential pitfalls of linear regression methods for estimating treatment effects with panel/longitudinal data.
- Apply novel DiD methods to estimate treatment effects when these effects can vary both across units and over time.
- Assess the plausibility of the identification assumptions behind DiD models and conduct sensitivity analyses to evaluate the robustness of estimation results.
Computing
The topics discussed in this course will be illustrated primarily through hands-on empirical applications using R and, in a few cases, Stata. Corresponding Stata code will also be provided where available and may be discussed during Q&A, depending on class interest.
You should be able to conduct some basic data manipulation and statistical analysis using either R or Stata.
If you’d like to use R for this course but don’t yet have much experience, here are some excellent online resources for building your R skills.
If you’d like to use Stata for this course but don’t yet have much experience, we recommend following along with a “getting started” video like the one here.
If you are not yet ready to purchase Stata, you may wish to take advantage of StataCorp’s software return policy. Student license purchases may be returned within 7 days of purchase. All other license purchases may be returned within 30 days of purchase.
The topics discussed in this course will be illustrated primarily through hands-on empirical applications using R and, in a few cases, Stata. Corresponding Stata code will also be provided where available and may be discussed during Q&A, depending on class interest.
You should be able to conduct some basic data manipulation and statistical analysis using either R or Stata.
If you’d like to use R for this course but don’t yet have much experience, here are some excellent online resources for building your R skills.
If you’d like to use Stata for this course but don’t yet have much experience, we recommend following along with a “getting started” video like the one here.
If you are not yet ready to purchase Stata, you may wish to take advantage of StataCorp’s software return policy. Student license purchases may be returned within 7 days of purchase. All other license purchases may be returned within 30 days of purchase.
Who should register?
This course is designed for researchers interested in conducting empirical analysis for causal inference and policy evaluation. While the course will be as self-contained as possible, you are expected to have a solid working knowledge of statistics (including sampling, expectation, variance, covariance, hypothesis testing, confidence intervals, and standard error estimation), multivariate linear regression, and a basic knowledge of panel/longitudinal data analysis.
This course is designed for researchers interested in conducting empirical analysis for causal inference and policy evaluation. While the course will be as self-contained as possible, you are expected to have a solid working knowledge of statistics (including sampling, expectation, variance, covariance, hypothesis testing, confidence intervals, and standard error estimation), multivariate linear regression, and a basic knowledge of panel/longitudinal data analysis.
Seminar outline
Day 1:
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- Potential outcomes, causal effects, and introduction to DiD models
- DiD models and event-study designs: simultaneous treatment adoption
Day 2:
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- Pre-trends and violations of the parallel-trends assumption
- DiD models and event-study designs: staggered treatment adoption
Day 3:
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- Synthetic control methods
- Further issues in DiD models
Day 1:
-
- Potential outcomes, causal effects, and introduction to DiD models
- DiD models and event-study designs: simultaneous treatment adoption
Day 2:
-
- Pre-trends and violations of the parallel-trends assumption
- DiD models and event-study designs: staggered treatment adoption
Day 3:
-
- Synthetic control methods
- Further issues in DiD models
Payment information
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Need funding from your employer or institution? Use our funding request template.
Our Tax ID number is 26-4576270.
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Need funding from your employer or institution? Use our funding request template.
Our Tax ID number is 26-4576270.