CausalModels (version 0.2.1)

ipweighting: Parametric IP Weighting

Description

`ipweighting` uses the propensity_scores function to generate inverse probability weights. The weights can either be standardized weights or non-standardized weights. The weights are used to train a general linear model whose coefficient for treatment represents the average treatment effect on the additive scale.

Usage

ipweighting(
  data,
  f = NA,
  family = gaussian(),
  p.f = NA,
  p.simple = pkg.env$simple,
  p.family = binomial(),
  p.scores = NA,
  SW = TRUE,
  n.boot = 0,
  ...
)

Value

ipweighting returns an object of class "ipweighting".

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

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

call

the matched call.

formula

the formula used in the model.

model

the underlying glm model.

weights

the estimated IP weights.

ATE

the estimated average treatment effect (risk difference).

ATE.summary

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

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.

p.f

(optional) an object of class "formula" that overrides the default formula for the denominator of the IP weighting function.

p.simple

a boolean indicator to build default formula with interactions for the propensity models. 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.

p.family

the family to be used in the underlying propensity model. By default, this is set to binomial.

p.scores

(optional) use calculated propensity scores for the weights. If using standardized weights, the numerator will still be modeled.

SW

a boolean indicator to indicate the use of standardized weights. By default, this is set to true.

n.boot

(optional) an integer value that indicates number of bootstrap iterations to calculate standard error. If no value is given, the standard error from the underlying linear model will be used.

...

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

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 <- ipweighting(data = nhefs.nmv)
summary(model)

# Model using calculated propensity scores and manual outcome formula
p.scores <- propensity_scores(nhefs.nmv)$p.scores
model <- ipweighting(wt82_71 ~ qsmk, p.scores = p.scores, data = nhefs.nmv)
summary(model)