prepare_data_causal {shapr} | R Documentation |
Generate data used for predictions and Monte Carlo integration for causal Shapley values
Description
This function loops over the given coalitions, and for each coalition it extracts the
chain of relevant sampling steps provided in internal$object$S_causal
. This chain
can contain sampling from marginal and conditional distributions. We use the approach given by
internal$parameters$approach
to generate the samples from the conditional distributions, and
we iteratively call prepare_data()
with a modified internal_copy
list to reuse code.
However, this also means that chains with the same conditional distributions will retrain a
model of said conditional distributions several times.
For the marginal distribution, we sample from the Gaussian marginals when the approach is
gaussian
and from the marginals of the training data for all other approaches. Note that
we could extend the code to sample from the marginal (gaussian) copula, too, when approach
is
copula
.
Usage
prepare_data_causal(internal, index_features = NULL, ...)
Arguments
internal |
List.
Not used directly, but passed through from |
index_features |
Positive integer vector. Specifies the id_coalition to
apply to the present method. |
... |
Currently not used. |
Value
A data.table containing simulated data that respects the (partial) causal ordering and the the confounding assumptions. The data is used to estimate the contribution function by Monte Carlo integration.
Author(s)
Lars Henry Berge Olsen