Propensity Score Analysis

Loading Map....

Date(s) - 10/26/2018 - 10/27/2018
9:00 am - 4:00 pm

Temple University Center City



Statistical Horizons is hosting a 2-day course titled “Propensity Score Analysis.” the course will be taught by Dr. Shenyang Guo starting on Friday October 26 at 9 AM.

Like regression analysis, propensity score analysis allows you to adjust for many potentially confounding variables. What makes propensity score methods different is that the results are easier to communicate to lay audiences. And propensity score estimates are often more robust to differences in the distributions of the confounding variables. This seminar provides a comprehensive introduction to propensity score methodology. It will focus on three closely related methods: propensity score matching and related methods, including greedy matching, optimal matching, and propensity score weighting, matching estimators, and propensity score analysis with non-parametric regression.

This seminar will focus on three closely related but technically distinct propensity score methods:

  • Propensity score matching and related methods, including greedy matching, optimal matching, and propensity score weighting using Stata psmatch2, pweights and R optmatch
  • Matching estimators using Stata nnmatch
  • Propensity score analysis with nonparametric regression using Stata psmatch2 and lowess.


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

The course does have a fee of $995, which includes all the course material. There is a early registration fee available until September 26. CLICK HERE to register for the course.

If there is an event or course that you want promoted on our site, that fits the theme of MethodSpace, we encourage you to send an email to so that it can go on our calendar.

Leave a Reply