CBPS.fit {CBPS}R Documentation

CBPS.fit determines the proper routine (what kind of treatment) and calls the approporiate function. It also pre- and post-processes the data

Description

CBPS.fit determines the proper routine (what kind of treatment) and calls the approporiate function. It also pre- and post-processes the data

Usage

CBPS.fit(treat, X, baselineX, diffX, ATT, method, iterations, standardize,
  twostep, sample.weights = sample.weights, ...)

Arguments

treat

A vector of treatment assignments. Binary or multi-valued treatments should be factors. Continuous treatments should be numeric.

X

A covariate matrix.

baselineX

Similar to baseline.formula, but in matrix form.

diffX

Similar to diff.formula, but in matrix form.

ATT

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.

method

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.

iterations

An optional parameter for the maximum number of iterations for the optimization. Default is 1000.

standardize

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.

twostep

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).

sample.weights

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().

Value

CBPS.fit object


[Package CBPS version 0.22 Index]