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.
prepare_data_causal(internal, index_features = NULL, ...)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.
List.
Not used directly, but passed through from explain().
Positive integer vector. Specifies the id_coalition to
apply to the present method. NULL means all coalitions. Only used internally.
Currently not used.
Lars Henry Berge Olsen