Understanding Propensity Score Matching
2023-03-19
Preamble
Description
Propensity score matching is widely used in analyzing observational datasets to reduce the impact of confounding due to observed covariates. This workshop will provide a basic overview of related causal inference concepts, explain propensity score matching analysis steps, illustrate propensity score matching diagnostics, and provide examples of when this method may be preferable to a regression.
Version history
Materials were updated over time through various deliveries of the content:
-
‘Understanding
Propensity Score Matching’:
- June 3, 2021, prepared as a Post Conference Workshop for 2021 Conference - CSEB.
-
‘A Practical
Introduction to Propensity Score Analysis using R’:
-
Sept 30, 2020, prepared for an invited webinar for Canadian Statistics Student
Society, in collaboration with TI Methods.
-
Sept 30, 2020, prepared for an invited webinar for Canadian Statistics Student
Society, in collaboration with TI Methods.
-
‘Introduction
to Causal Inference: Propensity Score Analysis in Healthcare Data’:
- May 14, 2020, prepared for Population Data BC (in partnership with IC/ES).
-
‘Introduction to Causal inference’
- March 20, 2023, March 21, 2022, March 22, 2021, March 23, 2020, March 25, 2019, prepared for Guest Lecture in SPPH 500/007 (Analytical Methods in Epidemiological Research) at UBC
Also see further resources at the very end of the document.
Prerequisites
The prerequisites are knowledge of multiple regression analysis and basic probability. Software demonstrations and codes will be provided in R, although proficiency in R is not required for understanding the concepts. If you are not familiar with R, and want to gain further understanding, I would suggest the following tutorial.
R tutorial
Karim ME, Hoang A and Qu Y “Introduction to R for health data analysis” URL: ehsanx.github.io/intro2R/
Packages that we will use in this demonstration:
# devtools::install_github('osofr/simcausal', build_vignettes = FALSE)
require(simcausal)
require(summarytools)
require(skimr)
require(jtools)
require(cobalt)
require(tableone)
require(MatchIt)
require(twang)
require(Matching)
require(SuperLearner)
require(ltmle)
require(DoubleML)
require(AIPW)
require(ggplot2)
library(mlr3)
library(mlr3learners)License
The online version of this book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. You may share, adapt the content and may distribute your contributions under the same license (CC BY-NC-SA 4.0), but you have to give appropriate credit, and cannot use material for the commercial purposes.
How to cite
Karim, ME (2021) “Understanding Propensity Score Matching”, URL: ehsanx.github.io/psw/
Comments
For any comments regarding this document, reach out to me.