Propensity score and Outcome Model
We estimate the propensity score and outcome (T-learner) plugin
estimates using an ensemble of machine learning models, including a wide
array of model complexities from linear models, GAMs, regularized
regressions. In this example, non-linear models are not included (due to
runtime), but they could easily be added by uncommenting the associated
lines.
Each individual component of the model provides a list of
hyperparameters, over which a full cross-product is taken and all
resulting models are estimate. For instance, SL.glmnet
sweeps over one hyperparameter (the mixing parameters between ridge and
Lasso). A model with each of the hyper-parameter values will be
estimated and incorporated into the ensemble. Note that
SL.glmnet automatically tunes the regularization parameter
using cv.glmnet, so this is not included as a
hyperparameter.
Quantities of interest
Quantities of Interest determine how results are reported to the
user. You can think about this as determining, for instance how results
should be plotted in a resulting chart.
For simplicity, this example simply provides results in one of two
ways: - Discrete covariates are stratified and the conditional effect is
plotted at each distinct level of the covariate. - Continuous covariates
have the effect surface estimated using local-linear regression via
nprobust of Calonico, Cattaneo and Farrell (2018). See,
similarly, Kennedy, Ma, McHugh and Small (2017) for justification of
this approach. Results are obtained for a grid of 100 quantiles across
the domain of the covariate.
An additional quantity of interest provided is the variable
importance of a learned joint model of conditional effects (over all
covariates). The approach implemented is described in Williamson,
Gilbert, Carone and Simon (2020).
hte_cfg <- basic_config() %>%
add_propensity_score_model("SL.glm.interaction") %>%
add_propensity_score_model("SL.glmnet", alpha = c(0,1)) %>%
add_propensity_score_model(
"SL.glmnet.interaction", alpha = c(0, 1)
) %>%
add_outcome_model("SL.glm.interaction") %>%
add_outcome_model("SL.glmnet", alpha = c(0, 1)) %>%
add_outcome_model("SL.glmnet.interaction", alpha = c(0, 1)) %>%
add_outcome_diagnostic("RROC") %>%
add_effect_model("SL.glm.interaction") %>%
add_effect_model("SL.glmnet", alpha = c(0, 1)) %>%
add_effect_model("SL.glmnet.interaction", alpha = c(0, 1)) %>%
add_effect_diagnostic("RROC") %>%
add_moderator("Stratified", x2, x3) %>%
add_moderator("KernelSmooth", x1, x4, x5) %>%
add_vimp(sample_splitting = FALSE) ->
hte_cfg
