https://github.com/aristotle-tek/tidyhte

https://github.com/aristotle-tek/tidyhte

Repository

https://github.com/aristotle-tek/tidyhte/blob/main/

# tidyhte



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`tidyhte` provides tidy semantics for estimation of heterogeneous
treatment effects through the use of [Kennedys (n.d.) doubly-robust
learner](https://arxiv.org/abs/2004.14497).

The goal of `tidyhte` is to use a sort of recipe design. This should
(hopefully) make it extremely easy to scale an analysis of HTE from the
common single-outcome / single-moderator case to many outcomes and many
moderators. The configuration of `tidyhte` should make it extremely easy
to perform the same analysis across many outcomes and for a wide-array
of moderators. Its written to be fairly easy to extend to different
models and to add additional diagnostics and ways to output information
from a set of HTE estimates.

The best place to start for learning how to use `tidyhte` are the
vignettes which runs through example analyses from start to finish:
`vignette("experimental_analysis")` and
`vignette("observational_analysis")`. There is also a writeup
summarizing the method and implementation in
`vignette("methodological-details")`.

# Installation

You will be able to install the released version of tidyhte from
[CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("tidyhte")
```

But this does not yet exist. In the meantime, install the development
version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("ddimmery/tidyhte")
```

# Setting up a configuration

To set up a simple configuration, its straightforward to use the Recipe
API:

``` r
library(tidyhte)
library(dplyr)

basic_config() %>%
    add_propensity_score_model("SL.glmnet") %>%
    add_outcome_model("SL.glmnet") %>%
    add_moderator("Stratified", x1, x2) %>%
    add_moderator("KernelSmooth", x3) %>%
    add_vimp(sample_splitting = FALSE) -> hte_cfg
```

The `basic_config` includes a number of defaults: it starts off the
SuperLearner ensembles for both treatment and outcome with linear models
(`"SL.glm"`)

# Running an Analysis

``` r
data %>%
    attach_config(hte_cfg) %>%
    make_splits(userid, .num_splits = 12) %>%
    produce_plugin_estimates(
        outcome_variable,
        treatment_variable,
        covariate1, covariate2, covariate3, covariate4, covariate5, covariate6
    ) %>%
    construct_pseudo_outcomes(outcome_variable, treatment_variable) -> data

data %>%
    estimate_QoI(covariate1, covariate2) -> results
```

To get information on estimate CATEs for a moderator not included
previously would just require rerunning the final line:

``` r
data %>%
    estimate_QoI(covariate3) -> results
```

Replicating this on a new outcome would be as simple as running the
following, with no reconfiguration necessary.

``` r
data %>%
    attach_config(hte_cfg) %>%
    produce_plugin_estimates(
        second_outcome_variable,
        treatment_variable,
        covariate1, covariate2, covariate3, covariate4, covariate5, covariate6
    ) %>%
    construct_pseudo_outcomes(second_outcome_variable, treatment_variable) %>%
    estimate_QoI(covariate1, covariate2) -> results
```

This leads to the ability to easily chain together analyses across many
outcomes in an easy way:

``` r
library("foreach")

data %>%
    attach_config(hte_cfg) %>%
    make_splits(userid, .num_splits = 12) -> data

foreach(outcome = list_of_outcomes, .combine = "bind_rows") %do% {
    data %>%
    produce_plugin_estimates(
        outcome,
        treatment_variable,
        covariate1, covariate2, covariate3, covariate4, covariate5, covariate6
    ) %>%
    construct_pseudo_outcomes(outcome, treatment_variable) %>%
    estimate_QoI(covariate1, covariate2) %>%
    mutate(outcome = rlang::as_string(outcome))
}
```

The function `estimate_QoI` returns results in a tibble format which
makes it easy to manipulate or plot results.

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