This adds a configuration attribute to a dataframe for HTE estimation. This configuration details the full analysis of HTE that should be performed.

attach_config(data, .HTE_cfg)

Arguments

data

dataframe

.HTE_cfg

HTE_cfg object representing the full configuration of the HTE analysis.

Details

For information about how to set up an HTE_cfg object, see the Recipe API documentation basic_config().

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.

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)
}
#> Loading required package: palmerpenguins
#> 
#> Attaching package: ‘palmerpenguins’
#> The following objects are masked from ‘package:datasets’:
#> 
#>     penguins, penguins_raw
#> Dropped 11 of 344 rows (3.2%) through listwise deletion.
#> 

#> estimating nuisance models [-----------------------------------] splits: 0 / 4
#> Loading required package: nnls
#> 

#> estimating nuisance models [========>--------------------------] splits: 1 / 4
#> 

#> estimating nuisance models [=================>-----------------] splits: 2 / 4
#> 

#> estimating nuisance models [=========================>---------] splits: 3 / 4
#> 

#> estimating nuisance models [===================================] splits: 4 / 4
#> 
                                                                              
#> 

#> Dropped 11 of 344 rows (3.2%) through listwise deletion.
#> Skipping diagnostic on .pseudo_outcome due to lack of model.
#> # A tibble: 11 × 5
#>    estimand       term                   level                estimate std_error
#>    <chr>          <chr>                  <chr>                   <dbl>     <dbl>
#>  1 MSE            body_mass_g            Control Response    99762.      1.05e+4
#>  2 MSE            body_mass_g            Treatment Response  95748.      1.01e+4
#>  3 SL risk        SL.glm.interaction_All Control Response   101913.      1.41e+3
#>  4 SL risk        SL.glm_All             Control Response   103644.      1.31e+3
#>  5 SL risk        SL.glm.interaction_All Treatment Response  98304.      4.39e+3
#>  6 SL risk        SL.glm_All             Treatment Response 101149.      3.93e+3
#>  7 SL coefficient SL.glm.interaction_All Control Response        0.603   5.72e-2
#>  8 SL coefficient SL.glm_All             Control Response        0.397   5.72e-2
#>  9 SL coefficient SL.glm.interaction_All Treatment Response      0.815   3.60e-2
#> 10 SL coefficient SL.glm_All             Treatment Response      0.185   3.60e-2
#> 11 SATE           NA                     NA                     11.1     3.42e+1