produce_plugin_estimates: Estimate models of nuisance functions

In tidyhte: Tidy Estimation of Heterogeneous Treatment Effects

Estimate models of nuisance functions

Description

This takes a dataset with an identified outcome and treatment column along with any number of covariates and appends three columns to the dataset corresponding to an estimate of the conditional expectation of treatment (.pi_hat), along with the conditional expectation of the control and treatment potential outcome surfaces (.mu0_hat and .mu1_hat respectively).

Usage

produce_plugin_estimates(data, outcome, treatment, ..., .weights = NULL)

Arguments

data	dataframe (already prepared with attach_config and make_splits)
outcome	Unquoted name of the outcome variable.
treatment	Unquoted name of the treatment variable.
...	Unquoted names of covariates to include in the models of the nuisance functions.
.weights	Unquoted name of weights column. If NULL, all analysis will assume weights are all equal to one and sample-based quantities will be returned.

Details

To see an example analysis, read vignette("experimental_analysis") in the context of an experiment, vignette("experimental_analysis") for an observational study, or vignette("methodological_details") for a deeper dive under the hood.

See Also

attach_config(), make_splits(), construct_pseudo_outcomes(), estimate_QoI()

Examples

library("dplyr")
if(require("palmerpenguins")) {
data(package = 'palmerpenguins')
penguins$unitid = seq_len(nrow(penguins))
penguins$propensity = rep(0.5, nrow(penguins))
penguins$treatment = rbinom(nrow(penguins), 1, penguins$propensity)
cfg <- basic_config() %>% 
add_known_propensity_score("propensity") %>%
add_outcome_model("SL.glm.interaction") %>%
remove_vimp()
attach_config(penguins, cfg) %>%
make_splits(unitid, .num_splits = 4) %>%
produce_plugin_estimates(outcome = body_mass_g, treatment = treatment, species, sex) %>%
construct_pseudo_outcomes(body_mass_g, treatment) %>%
estimate_QoI(species, sex)
}

tidyhte documentation built on Aug. 14, 2023, 5:08 p.m.