Skip to content
Livestream Seminar

Matching and Weighting for Causal Inference with R

A 4-Day Livestream Seminar Taught by

Stephen Vaisey
Course Dates:

Tuesday, August 23 –
Friday, August 26, 2022

Schedule: All sessions are held live via Zoom. All times are ET (New York time).
10:30am-12:30pm ET (convert to your local time)
1:30pm-3:00pm ET

This course offers an in-depth introduction to matching and weighting methods using R. Matching and weighting are quasi-experimental techniques for estimating causal effects from observational data using the potential outcomes or counterfactual framework. They are often (but not always) based on propensity scores. These techniques are now widely used in the social sciences, health sciences, management and public policy.

Starting August 23, 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.

More details about the course content

Computing

Who should register?

Seminar outline

Payment information

"I found the course approach very useful and practical as it showed how to handle complex real-world data as well as simpler examples.”

“This was a great course. I had fun learning new things. Dr. Vaisey is a great teacher, and I am not surprised that I am finding myself taking his courses again. I found the course approach very useful and practical as it showed how to handle complex real-world data as well as simpler examples.”

Tahereh Dehdarirad

Chalmers University of Technology

"It was just the right level of material, not too basic nor too advanced."

“I liked several points about this seminar: That Steve was not a statistician by training, so he had a very approachable way of covering the material; it was just the right level of material, not too basic nor too advanced; the range of techniques that were covered; how fun it was.”

Joana Cruz

University College of London

"I came away with an understanding of the core principles..."

“Steve introduced us to best practices in weighting and matching while making causal inferences. I appreciated his research expertise and practical insight when applying these techniques. I came away with an understanding of the core principles behind these approaches. I highly recommend this course for social science researchers who wish to make causal inferences with observational data.”

Bill Burns

CSUSM

“I liked the mix of lectures with exercises.”

“I liked the mix of lectures with exercises. Stephen’s teaching was also very nice with lots of examples and analogies.”

Andre Bedendo de Souza

University of York

“This course offered a good combination of theory/practice/R code."

“This course offered a good combination of theory/practice/R code. I also appreciated the ability to ask questions.”

Beth Chance

California Polytechnic State University

"The simple examples to illustrate different treatment effects was helpful as well."

“I liked seeing the practical applications in the examples during the lab and course work. The simple examples to illustrate different treatment effects was helpful as well.”

Johanna van Zyl

Baylor Scott & White Research Institute