standardization {CausalModels}

Parametric Standardization

Description

'standardization' uses a standard glm linear model to perform parametric standardization by adjusting bias through including all confounders as covariates. The model will calculate during training both the risk difference and the risk ratio. Both can be accessed from the model as well as estimates of the counterfactuals of treatment.

Usage

standardization(
  data,
  f = NA,
  family = gaussian(),
  simple = pkg.env$simple,
  n.boot = 50,
  ...
)

Arguments

data

a data frame containing the variables in the model. This should be the same data used in init_params.

f

(optional) an object of class "formula" that overrides the default parameter

family

the family to be used in the general linear model. By default, this is set to gaussian.

simple

a boolean indicator to build default formula with interactions. If true, interactions will be excluded. If false, interactions will be included. By default, simple is set to false. NOTE: if this is changed, the coefficient for treatment may not accurately represent the average causal effect.

n.boot

an integer value that indicates number of bootstrap iterations to calculate standard error.

...

additional arguments that may be passed to the underlying glm model.

Value

standardization returns an object of class "standardization".

The functions print, summary, and predict can be used to interact with the underlying glm model.

An object of class "standardization" is a list containing the following:

call

the matched call.

formula

the formula used in the model.

model

the underlying glm model.

ATE

a data frame containing estimates of the treatment effect of the observed, counterfactuals, and risk metrics.

ATE.summary

a data frame containing the ATE, SE, and 95% CI of the ATE.

Examples

library(causaldata)

data(nhefs)
nhefs.nmv <- nhefs[which(!is.na(nhefs$wt82)), ]
nhefs.nmv$qsmk <- as.factor(nhefs.nmv$qsmk)

confounders <- c(
  "sex", "race", "age", "education", "smokeintensity",
  "smokeyrs", "exercise", "active", "wt71"
)

init_params(wt82_71, qsmk,
  covariates = confounders,
  data = nhefs.nmv
)

# model using all defaults
model <- standardization(data = nhefs.nmv)
print(model)
summary(model)
print(model$ATE.summary)
print(model$ATE.summary$Estimate[[2]] -
  model$ATE.summary$Estimate[[3]]) # manually calculate risk difference