This function estimates the propensity of taking up treatment. The
user can choose from fitting a linear probability model, a logit
model, or a probit model. The function can also be used to generate
a table of propensity scores for a given set of covariates and
excluded variables. This was incorporated to account for the LATE
being a target parameter. Specifically, if the argument
formula is the name of a variable in
data, but the
target parameter is not the LATE, then no propensity model is
returned. If the target parameter is the LATE, then then the
propensity model is simply the empirical distribution of propensity
scores in the data conditioned on the set of covariates declared in
Formula characterizing probability model. If a
variable in the data already contains the propensity scores,
input the variable as a one-sided formula. For example, if the
Link function with which to estimate probability model. Can be chosen from "linear", "logit", or "probit".
A vector of variable names of excluded variables. This is required when the target parameter is the LATE.
A vector of variable names of non-excluded variables. This is required when the target parameter is the LATE, and the estimation procedure will condition on these variables.
environment, the environment for the original propensity score formula.
A vector of propensity scores for each observation, as well
as a 'model'. If the user inputs a formula characterizing the
model for taking up treatment, then the
object is returned. If the user declares a variable in the data
set to be used as the propensity score, then a
data.frame containing the propensity score for each
value of the covariates in the probability model is returned.
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