PSW

Propensity Score Weighting Methods for Dichotomous Treatments

Provides propensity score weighting methods to control for confounding in causal inference with dichotomous treatments and continuous/binary outcomes. It includes the following functional modules: (1) visualization of the propensity score distribution in both treatment groups with mirror histogram, (2) covariate balance diagnosis, (3) propensity score model specification test, (4) weighted estimation of treatment effect, and (5) doubly robust estimation of treatment effect. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) <DOI:10.1111/1468-0262.00442>, Li and Greene (2013) <DOI:10.1515/ijb-2012-0030>, and Li et al (2016) <DOI:10.1080/01621459.2016.1260466>.

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Package
PSW
Type
Package
Title
Propensity Score Weighting Methods for Dichotomous Treatments
Description
Provides propensity score weighting methods to control for confounding in causal inference with dichotomous treatments and continuous/binary outcomes. It includes the following functional modules: (1) visualization of the propensity score distribution in both treatment groups with mirror histogram, (2) covariate balance diagnosis, (3) propensity score model specification test, (4) weighted estimation of treatment effect, and (5) doubly robust estimation of treatment effect. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) , Li and Greene (2013) , and Li et al (2016) .
Version
1.1-2
Author
Huzhang Mao , Liang Li
Maintainer
Huzhang Mao
License
GPL (>= 2)
Depends
R (>= 3.3)
Imports
stats, Hmisc, gtools, graphics
Encoding
UTF-8
LazyData
true
LazyLoad
true
RoxygenNote
6.0.1
NeedsCompilation
no
Packaged
2017-10-09 16:27:49 UTC; hmao
Repository
CRAN
Date/Publication
2017-10-09 18:36:17 UTC

install.packages('PSW')

1.1-2

13 days ago

Huzhang Mao

GPL (>= 2)

Depends on

R (>= 3.3)

Imports

stats, Hmisc, gtools, graphics

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