Blackwell

This data set gives the outcomes a well as treatment assignments and covariates for the example from Blackwell (2013).

datasets

CBPS is an R package that implements the covariate
        balancing propensity score proposed by Imai and Ratkovic
        (2012).  The propensity score is estimated such that it
        maximizes the resulting covariate balance as well as the
        prediction of treatment assignment.  The method, therefore,
        avoids an iteration between model fitting and balance checking.
        Currently, the package can only deal with binary treatment in
        the cross section setting but in the future it will be extended
        to other settings including non-binary treatments and causal
        inference with longitudinal data.

Marc Ratkovic

CBPS

R Package for Covariate Balancing Propensity Score

Blackwell function

A data frame consisting of 13 columns (including treatment assignment, time, and identifier vectors) and 570 observations.

format

d.gone.neg is the treatment. d.gone.neg.l1, d.gone.neg.l2, and d.gone.neg.l3 are lagged treatment variables. camp.length, deminc, base.poll, base.und, and office covariates. year is the year of the particular race, and time goes from the first measurement (time = 1) to the election (time = 5). demName is the identifier, and demprcnt is the outcome.

Blackwell: Blackwell Data for Covariate Balancing Propensity Score

Description

Usage

Arguments

format

source

References