Preprocessing data through matching, weighting, or subclassification can be an effective way to reduce model dependence and improve efficiency when estimating the causal effect of a treatment (Ho, Imai, King, & Stuart, 2007). Propensity scores and other related methods (e.g., coarsened exact matching, Mahalanobis distance matching, genetic matching) have become popular in the social and health sciences as tools for this purpose. Two excellent introductions to propensity scores and other preprocessing methods are Stuart (2010) and Austin (2011), which describe them simply and clearly and point to other sources of knowledge.
The logic and theory behind preprocessing will not be discussed here, and reader’s knowledge of the causal assumption of strong ignorability is assumed.
The goal of finalpsm is to …
The finalpsm package assumes the user has sufficient understanding of
the theory and validity surrounding the use of propensity-score matching
for causal inference.
The output the propensity-score matching
Several packages in R exist to perform preprocessing and causal effect estimation, and some were reviewed by Keller & Tipton (2016). Of primary note are MatchIt (Ho, Imai, King, & Stuart, 2011), twang (Ridgeway, McCaffrey, Morral, Burgette, & Griffin, 2020), Matching (Sekhon, 2011), optmatch (Hansen & Klopfer, 2006), CBPS (Fong, Ratkovic, Hazlett, Yang, & Imai, 2019), ebal (Hainmueller, 2014), sbw (Zubizarreta & Li, 2019), designmatch (Zubizarreta, Kilcioglu, & Vielma, 2018), WeightIt (Greifer, 2020), MatchThem (Pishgar & Greifer, 2020), and cem (Iacus, King, & Porro, 2018); these together provide a near complete set of preprocessing tools in R to date.
Propensity score matching was used to reduce likely selection bias and balance variables between patients who were, or were not exposed to intravenous CT contrast. The propensity score was defined as the probability that a patient would be assigned to a particular group (contrast exposure or no exposure) based on the explanatory variables in the model. Patients who had incomplete data for explanatory variables required for matching were excluded from the analysis
You can install from GitHub with:
# install.packages("devtools")
devtools::install_github("kamclean/finalpsm")And the development version from