weighting: Estimate Weights for Generalizing Average Treatment Effect

In NUstat/generalizeR: Design a Sample Recruitment Plan and Assess Its 'Generalizability' to Broader Populations

Estimate Weights for Generalizing Average Treatment Effect

Description

Estimate Weights for Generalizing Average Treatment Effect

Usage

weighting(
  data,
  sample_indicator,
  treatment_indicator = NULL,
  outcome = NULL,
  covariates,
  estimation_method = "lr",
  disjoint_data = TRUE
)

Arguments

data	data frame comprised of "stacked" sample and target population data
sample_indicator	variable name denoting binary sample membership (1 = in sample, 0 = out of sample)
treatment_indicator	variable name denoting binary treatment assignment (ok if only available in sample, not population)
outcome	variable name denoting outcome
covariates	vector of covariate names in data set that predict sample membership
estimation_method	method to estimate the probability of sample membership (propensity scores). Default is logistic regression ("lr").Other methods supported are Random Forests ("rf") and Lasso ("lasso")
disjoint_data	logical. If TRUE, then sample and population data are considered disjoint. This affects calculation of the weights - see details for more information.

Value

A summary of propensity scores, covariates, and ASMD for both weighted and unweighted data, as well as a summary of the weights. Also weighted and unweighted TATE if outcome and treatment are given

Examples

library(tidyverse)

# creating a stratified sample and recruiting from the sample to prepare for assess
selection_covariates <- c("total", "pct_black_or_african_american", "pct_white",
                          "pct_female", "pct_free_and_reduced_lunch")

strat_output <- stratify(generalizeR:::inference_pop, guided = FALSE, n_strata = 4,
                         variables = selection_covariates, idvar = "ncessch")
rec_output <- recruit(strat_output, guided = FALSE, sample_size = 40)

# creating the sample dataset from the output of recruit
sample_list <- c(rec_output$recruitment_lists[[1]]$ncessch[1:5],
                  rec_output$recruitment_lists[[2]]$ncessch[1:20],
                  rec_output$recruitment_lists[[3]]$ncessch[1:11],
                  rec_output$recruitment_lists[[4]]$ncessch[1:4])
inference_pop_sample <- mutate(generalizeR:::inference_pop,
                               sample = if_else(ncessch %in% sample_list, 1, 0))

# weighting the sample with the given covariates
weighting_output <- weighting(inference_pop_sample, sample_indicator = "sample",
                              covariates = selection_covariates, disjoint_data = FALSE)

NUstat/generalizeR documentation built on Feb. 7, 2024, 6:39 p.m.