Difference in Differences - Online Course
A 4-Day Livestream Seminar Taught by
Gonzalo Vazquez-BareTuesday, July 16 –
Friday, July 19, 2024
11:00am-1:00pm (convert to your local time)
2:00pm-3:30pm
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 behind them is to compare the evolution over time of the outcome of a group of units (such as individuals, households, counties, firms, etc.) that are exposed to some intervention with the evolution of the outcomes of a group of units that are unaffected by the intervention. Under certain assumptions, DiD models allow the researcher to learn about the causal effect of the intervention by flexibly controlling for unobserved heterogeneity 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.
After a brief review of panel data methods, we will introduce the potential outcomes framework to rigorously define causal effects and to study classical results and recent advances in identification, estimation, and statistical inference for DiD models.
Starting July 16, we are offering this seminar as a 4-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
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.
- Provide empirical support for the validity of the identification assumptions behind DiD models and conduct sensitivity analysis to assess the robustness of the 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.
- Provide empirical support for the validity of the identification assumptions behind DiD models and conduct sensitivity analysis to assess the robustness of the estimation results.
Computing
The topics discussed in the course will be illustrated with hands-on, in-class empirical applications using R. The corresponding Stata code will also be made available.
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 with that package, here are some excellent on-line resources for building your R skills.
If you’d like to use Stata for this course but don’t yet have much experience with that package, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy.
The topics discussed in the course will be illustrated with hands-on, in-class empirical applications using R. The corresponding Stata code will also be made available.
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 with that package, here are some excellent on-line resources for building your R skills.
If you’d like to use Stata for this course but don’t yet have much experience with that package, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy.
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 and covariance, hypothesis testing, confidence intervals, standard error estimation) and multivariate linear regression.
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 and covariance, hypothesis testing, confidence intervals, standard error estimation) and multivariate linear regression.
Seminar outline
Day 1:
- Brief review of linear panel data methods
- Potential outcomes, causal effects, and introduction to DiD models
Day 2:
- DiD models and event-study designs: simultaneous treatment adoption
- Pre-trends and violations of the parallel-trends assumption
Day 3:
- DiD models and event-study designs: staggered treatment adoption
Day 4:
- Synthetic control methods
- Further issues in DiD models
Day 1:
- Brief review of linear panel data methods
- Potential outcomes, causal effects, and introduction to DiD models
Day 2:
- DiD models and event-study designs: simultaneous treatment adoption
- Pre-trends and violations of the parallel-trends assumption
Day 3:
- DiD models and event-study designs: staggered treatment adoption
Day 4:
- Synthetic control methods
- Further issues in DiD models
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.