backdr_exp: Compute Standardized Estimates With Parametric Exposure Model

In FrankLef/fciR: Data and functions used with Fundamentals of Causal Inference by Babette Brumback

Compute Standardized Estimates With Parametric Exposure Model

Description

Compute standardized estimates with parametric exposure model.

Usage

backdr_exp(data, formula, exposure.name, confound.names, att = FALSE)

standexp.r(data, formula, exposure.name, confound.names, att = FALSE)

Arguments

data	Dataframe of raw data.
formula	Formula representing the model.
exposure.name	Name of exposure variable.
confound.names	Names of the confound variables.
att	if FALSE calculate the standardized (unconfounded) causal effect. If TRUE calculate the average effect of treatment on the treated.

Details

The standardized estimates are computed using the exposure model. This method requires 2 different formulas which are created from the arguments formula. The 2 formulas created are for the exposure model and another one for the weighted linear model. Also T, i.e. the exposure, must always be binary, if not it can be made binary "one can first recode the data so that T = 1 when it is previously equaled, and T = 0 when it previously equaled any value other than t", p. 113.

Value

Dataframe in a useable format for rsample::bootstraps.

Source

Section 6.2.2

Examples

# An example can be found in the location identified in the
# source section above at the github site
# https://github.com/FrankLef/FundamentalsCausalInference.

FrankLef/fciR documentation built on Nov. 12, 2023, 6:09 a.m.