Check that all explicands has at least one valid MC sample in causal Shapley values
check_categorical_valid_MCsamp(dt, n_explain, n_MC_samples, joint_prob_dt)
dt: Data.table containing the generated MC samples (and conditional values) after each sampling stepn_MC_samples: Positive integer. For most approaches, it indicates the maximum number of samples to use in the Monte Carlo integration of every conditional expectation. For approach="ctree", n_MC_samples corresponds to the number of samples from the leaf node (see an exception related to the ctree.sample argument setup_approach.ctree()). For approach="empirical", n_MC_samples is the parameter in equations (14-15) of Aas et al. (2021), i.e. the maximum number of observations (with largest weights) that is used, see also the empirical.eta argument setup_approach.empirical().For undocumented arguments, see setup_approach.categorical().
Lars Henry Berge Olsen
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