hi_est: The Hirano and Imbens estimator

In bangecon/bsPDP: Bootstrapped PDPs with Confidence Intervals

Description

This function estimates the GPS function and estimates the ADRF. The GPS score is based on different treatment models. The treatment is linearly related to Xs.

Usage

hi_est(
  Y,
  treat,
  treat_formula,
  outcome_formula,
  data,
  grid_val,
  treat_mod,
  link_function,
  ...
)

Arguments

Y	is the the name of the outcome variable contained in data.
treat	is the name of the treatment variable contained in data.
treat_formula	an object of class "formula" (or one that can be coerced to that class) that regresses treat on a linear combination of X: a symbolic description of the model to be fitted.
outcome_formula	is the formula used for fitting the outcome surface. gps is one of the independent variables to use in the outcome_formula. ie. Y ~ treat+ I(treat^2) + gps + I(gps^2) + treat * gps or a variation of this. Use gps as the name of the variable representing the gps in outcome_formula.
data	is a dataframe containing Y, treat, and X.
grid_val	contains the treatment values to be evaluated.
treat_mod	a description of the error distribution to be used in the model for treatment. Options include: "Normal" for normal model, "LogNormal" for lognormal model, "Sqrt" for square-root transformation to a normal treatment, "Poisson" for Poisson model, "NegBinom" for negative binomial model, "Gamma" for gamma model, "Binomial" for binomial model.
link_function	For treat_mod = "Gamma" (fitted using glm) alternatives are "log" or "inverse". For treat_mod = "Binomial" (fitted using glm) alternatives are "logit", "probit", "cauchit", "log" and "cloglog".
...	additional arguments to be passed to the outcome lm() function.

Value

hi_est returns an object of class "causaldrf", a list that contains the following components:

param	parameter estimates for a hi fit.
t_mod	the result of the treatment model fit.
out_mod	the result of the outcome model fit.
call	the matched call.

##' @references Schafer, J.L., Galagate, D.L. (2015). Causal inference with a continuous treatment and outcome: alternative estimators for parametric dose-response models. Manuscript in preparation.

bangecon/bsPDP documentation built on Dec. 19, 2021, 6:41 a.m.