prepare_data_causal: Generate data used for predictions and Monte Carlo...

In shapr: Prediction Explanation with Dependence-Aware Shapley Values

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 explain().
index_features	Positive integer vector. Specifies the id_coalition to apply to the present method. NULL means all coalitions. Only used internally.
...	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

shapr documentation built on April 4, 2025, 12:18 a.m.