t_mod: A function to estimate conditional expected values and higher order moments

Description

This function fits a glm regression specified by the user to estimate conditional moments.

Usage

t_mod(treat,
      treat_formula,
      data,
      treat_mod,
      link_function,
      ...)

Value

t_mod returns a list containing the following elements:

T_data: a dataframe containing estimated treatment, estimated treatment squared, estimated treatment cube, estimated treatment quartic, and estimated gps.
T_result: the result of the treatment model fit.

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.
data: is a dataframe containing Y, 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, "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 low level treatment regression fitting functions.

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.

Examples

Run this code

## Example from Schafer (2015).

example_data <- sim_data

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

cond_exp_data <- t_mod_list$T_data

full_data <- cbind(example_data, cond_exp_data)

rm(example_data, t_mod_list, cond_exp_data, full_data)