overlap_fun {causaldrf}R Documentation

This function creates an overlapping dataset

Description

This function ensures that the units overlap according to the estimated gps values. The overlapping dataset depends on the number of classes n_class to subclassify on.

Usage

overlap_fun(Y,
            treat,
            treat_formula,
            data_set,
            n_class,
            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.

data_set

is a dataframe containing Y, treat, and X.

n_class

is the number of classes to split gps into.

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.

link_function

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

...

additional arguments to be passed to the treatment regression function

Value

overlap_fun returns a list containing the following elements:

overlap_dataset

dataframe containing overlapping data.

median_vec

a vector containing median values.

overlap_treat_result

the resulting treatment fit.

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.

Bia, Michela, et al. "A Stata package for the application of semiparametric estimators of dose response functions." Stata Journal 14.3 (2014): 580-604.

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

overlap_list <- overlap_fun(Y = Y,
                  treat = T,
                  treat_formula = T ~ B.1 + B.2 + B.3 + B.4 + B.5 + B.6 + B.7 + B.8,
                  data_set = example_data,
                  n_class = 3,
                  treat_mod = "Normal")

overlapped_data <- overlap_list$overlap_dataset
summary(overlapped_data)

rm(example_data, overlap_list, overlapped_data)

[Package causaldrf version 0.3 Index]