Propensity Score Analysis: Basics - Online Course
A 3-Day Livestream Seminar Taught by
Shenyang Guo10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
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 or register for both (email info@statisticalhorizons.com for a bundle discount), check out Propensity Score Analysis: Advanced.
Propensity score analysis (PSA) is a modern, innovative class of statistical methods that has become increasingly valuable for evaluating the effects of treatments, programs, or interventions using nonexperimental or observational data. While regression analysis is commonly used to adjust for potentially confounding variables, PSA offers a compelling alternative.
For students and professionals across disciplines—including those in business, economics, public policy, health, and the social sciences—PSA provides a practical way to draw credible insights from real-world data. It is especially useful when randomized experiments are not feasible, such as when assessing the impact of a marketing campaign, policy change, or service rollout. Results from PSA are often easier to communicate to decision-makers and more robust to differences in the underlying characteristics of the groups being compared. Most importantly, PSA focuses on modeling the assignment to treatment without considering outcomes, ensuring the objectivity of the study design.
This seminar will cover the basics of implementing propensity score analysis, including how to use logistic regression and generalized boosted regression to estimate propensity scores, and how to apply these scores to perform propensity score matching and related models.
Starting September 18, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
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:
-
- 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 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 before the seminar begins.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s 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 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 before the seminar begins.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s 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
- Estimating propensity scores with logistic regression and generalized boosted regression
Day 2:
-
- The classical matching methods: nearest neighbor within caliper matching and Mahalanobis metric matching
- The optimal matching methods
Day 3:
-
- The inverse probability of treatment weights estimator
- The Abadie and Imbens’s matching estimators
Day 1:
-
- Overview
- Counterfactual Frameworks and Assumptions
- Estimating propensity scores with logistic regression and generalized boosted regression
Day 2:
-
- The classical matching methods: nearest neighbor within caliper matching and Mahalanobis metric matching
- The optimal matching methods
Day 3:
-
- 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.