CBPS.fit determines the proper routine (what kind of treatment) and calls the approporiate function. It also pre- and post-processes the data
CBPS.fit(
treat,
X,
baselineX,
diffX,
ATT,
method,
iterations,
standardize,
twostep,
sample.weights = sample.weights,
...
)CBPS.fit object
A vector of treatment assignments. Binary or multi-valued treatments should be factors. Continuous treatments should be numeric.
A covariate matrix.
Similar to baseline.formula, but in matrix form.
Similar to diff.formula, but in matrix form.
Default is 1, which finds the average treatment effect on the treated interpreting the second level of the treatment factor as the treatment. Set to 2 to find the ATT interpreting the first level of the treatment factor as the treatment. Set to 0 to find the average treatment effect. For non-binary treatments, only the ATE is available.
Choose "over" to fit an over-identified model that combines the propensity score and covariate balancing conditions; choose "exact" to fit a model that only contains the covariate balancing conditions.
An optional parameter for the maximum number of iterations for the optimization. Default is 1000.
Default is TRUE, which normalizes weights to sum
to 1 within each treatment group. For continuous treatments, normalizes
weights to sum up to 1 for the entire sample. Set to FALSE to return
Horvitz-Thompson weights.
Default is TRUE for a two-step estimator, which will
run substantially faster than continuous-updating. Set to FALSE to
use the continuous-updating estimator described by Imai and Ratkovic (2014).
Survey sampling weights for the observations, if applicable. When left NULL, defaults to a sampling weight of 1 for each observation.
Other parameters to be passed through to optim().