scalar_wts {causaldrf}

This function calculates scalar weights for use in other models

Description

This function calculates the scalar weights

Usage

scalar_wts(treat,
           treat_formula,
           numerator_formula,
           data,
           treat_mod,
           link_function,
           ...)

Arguments

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 treat, and X.

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, "Poisson" for Poisson model, "Sqrt" for square-root transformation to a normal treatment, "NegBinom" for negative binomial model, "Gamma" for gamma model.

link_function

is either "log", "inverse", or "identity" for the "Gamma" treat_mod.

...

additional arguments to be passed to the treatment regression fitting function.

Value

scalar_wts returns an object of class "causaldrf_wts", a list that contains the following components:

param

summary of estimated weights.

t_mod

the result of the treatment model fit.

num_mod

the result of the numerator model fit.

weights

estimated weights for each unit.

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.

See Also

iptw_est, ismw_est, reg_est, aipwee_est, wtrg_est, etc. for other estimates.

t_mod, overlap_fun to prepare the data for use in the different estimates.

Examples

## Example from Schafer (2015).

example_data <- sim_data

scalar_wts_list <- scalar_wts(treat = T,
                     treat_formula = T ~ B.1 + B.2 + B.3 + B.4 + B.5 + B.6 + B.7 + B.8,
                     numerator_formula = T ~ 1,
                     data = example_data,
                     treat_mod = "Normal")

sample_index <- sample(1:1000, 100)

plot(example_data$T[sample_index],
     scalar_wts_list$weights[sample_index],
     xlab = "T",
     ylab = "weights",
     main = "scalar_wts")


rm(example_data, scalar_wts_list, sample_index)