Causal Inference in Econometrics - Online Course
A 4-Week On-Demand Seminar Taught by
Nick Huntington-KleinEach Monday you will receive an email with instructions for the following week.
All course materials are available 24 hours a day. Materials will be accessible for an additional 2 weeks after the official close on September 25.
Econometrics is a broad category of data analysis that focuses on trying to use data to understand how the world works, even in cases where you can’t run an experiment. This course offers a survey of econometrics. It begins with a brief review of regression, but mostly focuses on research design in econometrics and methods commonly used to estimate causal effects, including fixed effects, difference-in-differences, instrumental variables, and regression discontinuity. The seminar puts an emphasis on practical understanding and use of these concepts, as opposed to statistical proofs.
The course takes place online in a series of four weekly installments of videos, readings, and exercises, and requires about 6-8 hours/week. You may participate at your own convenience; there are no set times when you are required to be online.
This four-week course can be accessed with any recent web browser on almost any platform, including iPhone, iPad, and Android devices. It consists of several modules, which contain videos of the 4-day livestream version of the course in its entirety. There are also weekly exercises that ask you to apply what you’ve learned to a real data set.
There is also an online discussion forum where you can post questions or comments about any aspect of the course. All questions will be promptly answered by Dr. Huntington-Klein.
More details about the course content
Regression is the primary tool that econometricians use to evaluate data. We’ll be going over how regression is used in econometrics, including the many ways econometricians grapple with the parts of the world we don’t understand yet – error terms. Identification is how econometricians link theory (economic theory or otherwise) to data, and determine not just the difference between correlation and causation, but more broadly whether our analysis is actually answering the question we want it to. Identification is a broad idea, but there are a few standard research designs that can help us a lot, and we’ll be covering modern developments in fixed effects, difference-in-differences, regression discontinuity, and instrumental variables.
Specific topics covered will be linear regression, identification, omitted variable bias, directed acyclic graphs, fixed effects, difference-in-differences, regression discontinuity, and instrumental variables. There will also be a brief overlook on how machine learning is likely to change how econometrics is performed.
Demonstrations emphasize the R programming language. However, all materials will also be made available in Stata and Python.
Regression is the primary tool that econometricians use to evaluate data. We’ll be going over how regression is used in econometrics, including the many ways econometricians grapple with the parts of the world we don’t understand yet – error terms. Identification is how econometricians link theory (economic theory or otherwise) to data, and determine not just the difference between correlation and causation, but more broadly whether our analysis is actually answering the question we want it to. Identification is a broad idea, but there are a few standard research designs that can help us a lot, and we’ll be covering modern developments in fixed effects, difference-in-differences, regression discontinuity, and instrumental variables.
Specific topics covered will be linear regression, identification, omitted variable bias, directed acyclic graphs, fixed effects, difference-in-differences, regression discontinuity, and instrumental variables. There will also be a brief overlook on how machine learning is likely to change how econometrics is performed.
Demonstrations emphasize the R programming language. However, all materials will also be made available in Stata and Python.
Computing
This is a hands-on course with instructor-led software demonstrations and guided exercises. These guided exercises will be primarily designed for the R language, so you should use a computer with a recent version of R (version 4.0.0 or later) and RStudio (version 1.4 or later). However, if you prefer and do not mind deviating slightly from the guided exercises, all exercises will also be available for Stata (version 13 or later) and Python (version 3.7 or later).
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.
This is a hands-on course with instructor-led software demonstrations and guided exercises. These guided exercises will be primarily designed for the R language, so you should use a computer with a recent version of R (version 4.0.0 or later) and RStudio (version 1.4 or later). However, if you prefer and do not mind deviating slightly from the guided exercises, all exercises will also be available for Stata (version 13 or later) and Python (version 3.7 or later).
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.
Who should register?
You should take this course if you want to understand the how and why of econometric analysis of observational data. If you want to understand what these tools actually do and how they answer important questions, you should enroll. You should have a basic working knowledge of your language of choice (R, Stata, or Python). Extensive programming experience is not necessary.
This course does not require calculus or familiarity with statistical proofs, and is appropriate for researchers (in public-sector, or private-sector domains, or students or faculty in academia) who have a working knowledge of statistics. Familiarity with linear regression is even better.
You should take this course if you want to understand the how and why of econometric analysis of observational data. If you want to understand what these tools actually do and how they answer important questions, you should enroll. You should have a basic working knowledge of your language of choice (R, Stata, or Python). Extensive programming experience is not necessary.
This course does not require calculus or familiarity with statistical proofs, and is appropriate for researchers (in public-sector, or private-sector domains, or students or faculty in academia) who have a working knowledge of statistics. Familiarity with linear regression is even better.
Seminar outline
1. Linear regression
-
- Theoretical and statistical models
- Line-fitting
- Interpreting regressions
2. Identification
-
- What is identification?
- Causal diagrams
- Back-door paths and omitted variable bias
- Identification using control variables
- Placebo tests
3. Common back-door designs:
-
- Fixed effects
- Difference-in-differences
- Estimation
- Common problems and solutions
4. Common front-door designs:
-
- Instrumental variables
- Regression discontinuity
- Estimation
- Common problems and solutions
5. Machine learning: a glimpse of the future
1. Linear regression
-
- Theoretical and statistical models
- Line-fitting
- Interpreting regressions
2. Identification
-
- What is identification?
- Causal diagrams
- Back-door paths and omitted variable bias
- Identification using control variables
- Placebo tests
3. Common back-door designs:
-
- Fixed effects
- Difference-in-differences
- Estimation
- Common problems and solutions
4. Common front-door designs:
-
- Instrumental variables
- Regression discontinuity
- Estimation
- Common problems and solutions
5. Machine learning: a glimpse of the future
Registration instructions
The fee of $695 (USD) includes all course materials. All major credit cards are accepted.
This course is hosted on a platform called DigitalChalk. To register, you’ll need to go to statisticalhorizons.digitalchalk.com and click on Create Account. Then you will enter your name and email address, and create a password. Be sure to save your password because you will need it to logon to the course itself.
When you have created your account, you’ll be taken to your new home page. Click on the Register Now button (or click the Catalog icon on the left-hand column), and you’ll see “Causal Inference in Econometrics” as one of the available courses. At the bottom of the box for that course, click the green button Add to Cart. Next click the green button at the top that says Checkout. You will then be prompted for your credit card information.
When you have finished the payment process, you will be taken back to your home page. Click on Dashboard to see Causal Inference in Econometrics. When the course begins on August 28, you can click the play button to get started.
The fee of $695 (USD) includes all course materials. All major credit cards are accepted.
This course is hosted on a platform called DigitalChalk. To register, you’ll need to go to statisticalhorizons.digitalchalk.com and click on Create Account. Then you will enter your name and email address, and create a password. Be sure to save your password because you will need it to logon to the course itself.
When you have created your account, you’ll be taken to your new home page. Click on the Register Now button (or click the Catalog icon on the left-hand column), and you’ll see “Causal Inference in Econometrics” as one of the available courses. At the bottom of the box for that course, click the green button Add to Cart. Next click the green button at the top that says Checkout. You will then be prompted for your credit card information.
When you have finished the payment process, you will be taken back to your home page. Click on Dashboard to see Causal Inference in Econometrics. When the course begins on August 28, you can click the play button to get started.