Propensity Score Analysis: Basics - Online Course
A 4-Day Livestream Seminar Taught by
Shenyang Guo10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
NOTE: this course is designed for those who have no previous experience with propensity score analysis. If you are looking to learn more advanced methods, check out Propensity Score Analysis: Advanced.
Propensity score analysis is a relatively new and innovative class of statistical methods that has proven useful for evaluating the effects of treatments or interventions when using nonexperimental or observational data. Although regression analysis is most often used to adjust for potentially confounding variables, propensity score analysis is an attractive alternative. Results produced by propensity score methods are typically easier to communicate to lay audiences. And propensity score estimates are often more robust to differences in the distributions of the confounding variables across the groups being compared. Most important, in PSA we model the assignment to treatment without knowledge of the outcomes, thereby ensuring the objectivity of the design.
This seminar will focus on basics of running propensity score analysis. By taking this seminar, you will learn how to use logistic regression and generalized boosted regression to estimate propensity scores, and how to use the estimated propensity scores to run propensity score matching and related models.
Starting June 6, 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
This seminar will focus on two methods to estimate propensity scores, and four methods to run corrective models of outcome analysis to enhance the study’s internal validity:
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- Using logistic regression and generalized boosted regression to estimate the propensity scores.
- The classic matching methods, including nearest neighbor within caliper matching and Mahalanobis metric matching.
- The optimal matching methods.
- The inverse probability of treatment weights estimator, also known as propensity score weighting method.
- The Abadie and Imbens’s matching estimators.
The examination of these methods will be guided by the Neyman-Rubin counterfactual framework.
This seminar will focus on two methods to estimate propensity scores, and four methods to run corrective models of outcome analysis to enhance the study’s internal validity:
-
- Using logistic regression and generalized boosted regression to estimate the propensity scores.
- The classic matching methods, including nearest neighbor within caliper matching and Mahalanobis metric matching.
- The optimal matching methods.
- The inverse probability of treatment weights estimator, also known as propensity score weighting method.
- The Abadie and Imbens’s matching estimators.
The examination of these methods will be guided by the Neyman-Rubin counterfactual framework.
Computing
The seminar uses Stata and R software packages to demonstrate the implementation of propensity score analysis. All Stata and R syntax files and illustrative data can be downloaded at the Propensity Score Analysis Support Site. You are strongly encouraged to use a computer with Stata or R installed. To follow along with the course exercises, you should be able to perform basic data manipulation and analyses in Stata or R.
If you’d like to take this course but are concerned that you don’t know enough Stata, we recommend following along with a “getting started” video like the one here before the seminar begins.
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.
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.
The seminar uses Stata and R software packages to demonstrate the implementation of propensity score analysis. All Stata and R syntax files and illustrative data can be downloaded at the Propensity Score Analysis Support Site. You are strongly encouraged to use a computer with Stata or R installed. To follow along with the course exercises, you should be able to perform basic data manipulation and analyses in Stata or R.
If you’d like to take this course but are concerned that you don’t know enough Stata, we recommend following along with a “getting started” video like the one here before the seminar begins.
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.
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?
The seminar will be helpful to researchers who are engaged in intervention research, program evaluation, or more generally causal inference, when their data were not generated by a randomized clinical trial. The prerequisite for taking this seminar is knowledge of multiple regression analysis. Researchers from economics, public health, epidemiology, psychology, sociology, social work, medical research, education, and similar disciplines may consider participating.
The seminar will be helpful to researchers who are engaged in intervention research, program evaluation, or more generally causal inference, when their data were not generated by a randomized clinical trial. The prerequisite for taking this seminar is knowledge of multiple regression analysis. Researchers from economics, public health, epidemiology, psychology, sociology, social work, medical research, education, and similar disciplines may consider participating.
Seminar outline
Day 1
-
- Overview
- Counterfactual frameworks and assumptions
Day 2
-
- Estimating propensity scores with logistic regression and generalized boosted regression
- The classical matching methods: nearest neighbor within caliper matching and Mahalanobis metric matching
Day 3
-
- The optimal matching methods
Day 4
-
- The inverse probability of treatment weights estimator
- The Abadie and Imbens’s matching estimators
Day 1
-
- Overview
- Counterfactual frameworks and assumptions
Day 2
-
- Estimating propensity scores with logistic regression and generalized boosted regression
- The classical matching methods: nearest neighbor within caliper matching and Mahalanobis metric matching
Day 3
-
- The optimal matching methods
Day 4
-
- The inverse probability of treatment weights estimator
- The Abadie and Imbens’s matching estimators
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.