- Improving Causal Inference in Observational Studies: Propensity Score Matching
-
Min Heui Yu, Dae Ryong Kang
-
Cardiovasc Prev Pharmacother. 2019;1(2):57-62. Published online October 31, 2019
-
DOI: https://doi.org/10.36011/cpp.2019.1.e6
-
-
3,431
View
-
53
Download
-
1
Citations
-
 Abstract
 PDF
- Propensity score matching (PSM) is a useful statistical methods to improve causal inference in observational studies. It guarantees comparability between 2 comparison groups are required. PSM is based on a “counterfactual” framework, where a causal effect on study participants (factual) and assumed participants (counterfactual) are compared. All participants are divided into 2 groups with the same covariates matched as much as possible. Propensity score is used for matching, and it reflects the conditional probabilities that individuals will be included in the experimental group when covariates are controlled for all subjects. The counterfactuals for the experimental group are matched between groups with characteristics as similar as possible. In this article, we introduce the concept of PSM, PSM methods, limitations, and statistical tools.
-
Citations
Citations to this article as recorded by  - Strengthening Association through Causal Inference
Megan Lane, Nicholas L. Berlin, Kevin C. Chung, Jennifer F. Waljee
Plastic & Reconstructive Surgery.2023;[Epub] CrossRef
|
