Propensity Score Matching
May 13, 2024
2 minute read
Propensity score matching (PSM) is a statistical technique that is used to estimate the causal effect of a treatment or intervention on an outcome. It is a non-experimental method, which means that it does not require a randomized controlled trial (RCT) to be conducted. Instead, PSM uses observational data to create a comparison group that is similar to the treatment group in terms of all observed confounders. This allows the researcher to estimate the causal effect of the treatment by comparing the outcomes of the treatment group to the outcomes of the comparison group.
Why learn Propensity Score Matching?
There are several reasons why someone might want to learn about propensity score matching. First, PSM is a powerful tool for estimating the causal effect of a treatment or intervention. It can be used to evaluate the effectiveness of new programs and policies, and to compare the effectiveness of different treatments. Second, PSM is relatively easy to implement. It can be done using a variety of statistical software packages, and there are many resources available to help researchers learn how to use PSM.
How online courses can help you learn Propensity Score Matching
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Find a path to becoming a Propensity Score Matching. Learn more at:
OpenCourser.com/topic/65wzrd/propensity
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Propensity Score Matching.
Provides a comprehensive overview of propensity score matching, including its assumptions, estimation methods, and applications. It is written by one of the leading experts in the field and is considered a foundational text on the topic.
Provides a broad overview of causal inference, including propensity score matching. It is written by three leading experts in the field and is considered a classic text on the topic.
Provides a comprehensive overview of propensity score analysis, including both theoretical and applied aspects. It is written by three leading experts in the field and is considered a standard textbook on the topic.
Provides a comprehensive overview of the design and analysis of randomized experiments. It includes a chapter on propensity score matching, which is used to estimate the causal effect of a treatment when randomization is not possible.
This handbook provides a comprehensive overview of applied spatial analysis, including a chapter on propensity score matching. It is written by three leading experts in the field and is considered a standard reference on the topic.
Provides a comprehensive overview of statistical methods for comparative studies, including a chapter on propensity score matching. It is written by three leading experts in the field and is considered a standard textbook on the topic.
Provides a comprehensive overview of quantitative methods in political science, including a chapter on propensity score matching. It is written by three leading experts in the field and is considered a standard textbook on the topic.
This handbook provides a comprehensive overview of research methods in public health, including a chapter on propensity score matching. It is written by three leading experts in the field and is considered a standard reference on the topic.
This handbook provides a comprehensive overview of research synthesis and meta-analysis, including a chapter on propensity score matching. It is written by three leading experts in the field and is considered a standard reference on the topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/65wzrd/propensity
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