Propensity Score Matching

Propensity score matching (PSM) is a technique that simulates an experimental study in an observational data set in order to estimate a causal effect. In an experimental study, subjects are randomly allocated to “treatment” and “control” groups; if the randomisation is done correctly, there should be no differences in the background characteristics of the treated and non-treated groups, so any differences in the outcome between the two groups may be attributed to a causal effect of the treatment. An observational survey, by contrast, will contain some people who have been subject to the “treatment” and some people who have not, but they will not have not been randomly allocated to those groups. The characteristics of people in the treatment and control groups may differ, so differences in the outcome cannot be attributed to the treatment. PSM attempts to mimic the experimental situation trial by creating two groups from the sample, whose background characteristics are virtually identical. People in the treatment group are “matched” with similar people in the control group. The difference between the treatment and control groups in this case should may therefore more plausibly be attributed to the treatment itself. PSM is widely applied in many disciplines, including sociology, criminology, economics, politics, and epidemiology. The module covers the basic theory of PSM, the steps in the implementation (e.g. variable choice for matching and types of matching algorithms), and assessment of matching quality. We will also work through practical exercises using Stata, in which students will learn how to apply the technique to the analysis of real data and how to interpret the results.

• University Students from Tier 1 Departments
• Further details regarding eligibility criteria are available here

Students wishing to take this module should have either successfully completed Doing Multivariate Analysis, including the end-of-module test, or have had previous equivalent training in statistics (verified by the Skill Check). You will need to be confident in the use of Stata; if you are not, then please take the SSRMC’s Introduction to Stata or 90-minute Stata courses.

Number of sessions: 2

• Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22(1), 31-72.
• Dehejia, R.H., & Wahba, S. (2002). Propensity score-matching methods for nonexperimental causal studies. The Review of Economics and Statistics, 84(1), 151-161.
• Guo, S., & Fraser, M.W. (2010). Propensity score analysis: Statistical methods and applications. SAGE Publications Ltd. USA.
• Morgan, S.L., & Winship, C. (2007). Counterfactuals and casual inference: Methods and principles for social research. Cambridge University Press.
• Rosenbaum, P.R., & Rubin, D.B. (1983). The central role of the propensity score in observational studies for casual effects. Biometrika, 70(1), 41-55.
• Rubin, D.B. (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services & Outcomes Research Methodology, 2, 169-188.

There may be an online open-book test at the end of the module; for most students, the test is not compulsory.

Click the "Booking" panel on the left-hand sidebar (on a phone, this will be via a link called Booking/Availability near the top of the page).

8 hours - A morning lecture and an afternoon lab session
This is an intensive, one-day module