Treatment Effects Analysis - Online Course
A 4-Week On-Demand Seminar Taught by
Stephen VaiseyEach 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 December 18.
This seminar focuses on matching and weighting cros
The course takes place online in a series of four weekly installments of videos, quizzes, and assignments, 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 12 modules:
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- The potential outcomes framework
- Computing treatment effects manually
- Exact matching
- Propensity scores
- Propensity score matching
- Balancing checking
- Inverse probability weighting
- Covariate balancing propensity scores and entropy balancing
- Coarsened exact matching
- Doubly robust estimation
- Adding regression adjustment
- Loose ends and extensions
Each module consists of a video segment recorded from the 3-day, livestream version of the course. The modules contain all the lectures and slides from that course.
After each module there is a short multiple-choice quiz to test your knowledge. There are also exercises in which you can apply what you’ve learned to a real data set. An online discussion forum is available for you to post questions or comments about any aspect of the course. All questions will be answered by Dr. Vaisey in a timely manner.
More details about the course content
The goal of treatment-effects analysis is to identify the causal effect of a treatment on an outcome, such as the effect of a college education on earnings, the effect of divorce on child outcomes, or the effect of a training program on employee productivity. A major advantage of treatment-effects techniques over standard regression methods is that they can produce different estimates of causal effects for subjects who are likely to receive the treatment and for those who are unlikely to receive it, an important distinction for policy work.
This seminar will take participants from simple exact matching to recent developments like coarsened exact matching, doubly-robust estimators, and entropy balancing. Participants will get extensive practical experience by working through case studies from economics, sociology, medicine, and public health.
We will cover a variety of topics including exact matching, propensity score matching and weighting, other forms of non-parametric matching and weighting, regression adjustment, and various forms of doubly-robust estimators. We will also consider tests for violations of assumptions and ways to test the sensitivity of results to violations of untestable assumptions. Although we will focus primarily on binary treatments, we will briefly explore how these techniques can be applied to multivalued treatments as well.
The goal of treatment-effects analysis is to identify the causal effect of a treatment on an outcome, such as the effect of a college education on earnings, the effect of divorce on child outcomes, or the effect of a training program on employee productivity. A major advantage of treatment-effects techniques over standard regression methods is that they can produce different estimates of causal effects for subjects who are likely to receive the treatment and for those who are unlikely to receive it, an important distinction for policy work.
This seminar will take participants from simple exact matching to recent developments like coarsened exact matching, doubly-robust estimators, and entropy balancing. Participants will get extensive practical experience by working through case studies from economics, sociology, medicine, and public health.
We will cover a variety of topics including exact matching, propensity score matching and weighting, other forms of non-parametric matching and weighting, regression adjustment, and various forms of doubly-robust estimators. We will also consider tests for violations of assumptions and ways to test the sensitivity of results to violations of untestable assumptions. Although we will focus primarily on binary treatments, we will briefly explore how these techniques can be applied to multivalued treatments as well.
Computing
In the videos, Stata (version 14 or higher) will be the main software package used to demonstrate the methods. Stata 13 can do at least 95% of what we will cover in this course. Some R will also be used to demonstrate techniques.
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.
In the videos, Stata (version 14 or higher) will be the main software package used to demonstrate the methods. Stata 13 can do at least 95% of what we will cover in this course. Some R will also be used to demonstrate techniques.
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 for any who want to learn to apply this family of techniques to observational data. Participants should have a basic foundation in linear and logistic regression.
This course is for any who want to learn to apply this family of techniques to observational data. Participants should have a basic foundation in linear and logistic regression.
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 “Treatment Effects Analysis” 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 Treatment Effects Analysis. When the course begins on November 20, 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 “Treatment Effects Analysis” 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 Treatment Effects Analysis. When the course begins on November 20, you can click the play button to get started.