t_mod {causaldrf}  R Documentation 
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,
...)
Arguments
treat 
is the name of the treatment variable contained in 
treat_formula 
an object of class "formula" (or one that can be coerced to that class)
that regresses 
data 
is a dataframe containing 
treat_mod 
a description of the error distribution
to be used in the model for treatment. Options include:

link_function 
is either "log", "inverse", or "identity" for the "Gamma" 
... 
additional arguments to be passed to the low level treatment regression fitting functions. 
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. 
References
Schafer, J.L., Galagate, D.L. (2015). Causal inference with a continuous treatment and outcome: alternative estimators for parametric doseresponse models. Manuscript in preparation.
See Also
ismw_est
, reg_est
,
wtrg_est
, aipwee_est
, etc. for other estimates.
overlap_fun
to prepare the data
for use in the different estimates.
Examples
## 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)