backdr_exp_np: Compute Standardized Averages Using Exposure Modeling, Non...

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

Compute Standardized Averages Using Exposure Modeling, Non Parametric

Description

Compute standardized averages using exposure modeling, non-parametric.

Usage

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

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

attsem.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

Compute standardized averages using exposure modeling. See introduction of section 6.2 and section 6.2.1.

Value

Dataframe in a useable format for rsample::bootstraps.

Source

Section 6.2.1

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.