simulate_causal_experiment: simulate a RCT or observational data for causal effect...

Description Usage Arguments Value

View source: R/ExmpleSetups_causaleffects.R

Description

This function simulates a RCT or observational data for causal effect estimation to test different heterogenuous treatment effect estimation strategies.

Usage

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simulate_causal_experiment(ntrain, ntest, dim = ncol(given_features),
  alpha = 0.1, feat_distribution = "normal", given_features = NULL,
  setup = "RespSparseTau1strong", testseed = NULL, trainseed = NULL)

Arguments

ntrain

Number of training examples.

ntest

Number of test examples.

dim

Dimension of the data set.

alpha

Only used if given_features is not set and feat_distribution is chosen to be normal. It specifies how correlated the features can be. If alpha = 0, then the features are independent if alpha is very large, then the features can be very correlated.

feat_distribution

Only used if given_features is not specified. Either "normal" or "unif". It specifies the distribution of the features.

given_features

This is used, if we already have features and want to test the performance of different estimators for a particular set of features.

setup

This is used to specify the function form of the potential outcomes and the treatment assignment. One of RespSparseTau1strong, RsparseT2weak, complexTau, Conf1, rare1, STMpp, Ufail, Usual1, WA1, WA2, WA3.

testseed

is the seed used to generate the testing data, if NULL, then the seed of the main session is used.

trainseed

is the seed used to generate the training data, if NULL, then the seed of the main session is used.

Value

A list of the transformed object 'x', and encoding information 'labels'.


soerenkuenzel/hte documentation built on June 12, 2018, 4:26 p.m.