iptw_est: The iptw method or importance weighting method estimates the...

In bangecon/bsPDP: Bootstrapped PDPs with Confidence Intervals

Description

The iptw method or importance weighting method estimates the ADRF by weighting the data with stabilized or non-stabilized weights.

Usage

iptw_est(
  Y,
  treat,
  treat_formula,
  numerator_formula,
  data,
  degree,
  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.
numerator_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. i.e. treat ~ 1.
data	is a dataframe containing Y, treat, and X.
degree	is 1 for linear and 2 for quadratic outcome model.
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, "Ordinal" for ordinal model, "Multinomial" for multinomial model.
link_function	specifies the link function between the variables in numerator or denominator and exposure, respectively. 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". For treat_mod = "Multinomial" this argument is ignored, and multinomial logistic regression models are always used (fitted using multinom). For treat_mod = "Ordinal" (fitted using polr) alternatives are "logit", "probit", "cauchit", and "cloglog".
...	additional arguments to be passed to the low level treatment regression fitting functions.

Value

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

param	parameter estimates for a iptw fit.
t_mod	the result of the treatment model fit.
num_mod	the result of the numerator model fit.
weights	the estimated weights.
weight_data	the weights.
out_mod	the outcome model.

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.