generate_cre_dataset {CRE}

R Documentation

Generate CRE synthetic data

Description

Generates synthetic data with continues or binary outcome.

Usage

generate_cre_dataset(
  n = 1000,
  rho = 0,
  n_rules = 2,
  p = 10,
  effect_size = 2,
  binary_covariates = TRUE,
  binary_outcome = TRUE,
  confounding = "no"
)

Arguments

n	An integer number that represents the number of observations. Non-integer values will be converted into an integer number.
rho	A positive double number that represents the correlation within the covariates (default: 0, range: (0,1)).
n_rules	The number of causal rules. (default: 2, range: 1,2,3,4).
p	The number of covariates (default: 10).
effect_size	The effect size magnitude in (default: 2, range: >=0).
binary_covariates	Whether to use binary or continuous covariates (default: TRUE).
binary_outcome	Whether to use binary or continuous outcomes (default: TRUE).
confounding	Only for continuous outcome, add confounding variables: Linear confounding "lin". Non-linear confounding "nonlin". No confounding "no" (default).

Value

A list of synthetic data containing:

• An outcome vector (y),

• A treatment vector (z),

• A covariates matrix (X) and

• An individual treatment vector (ite)

Note

Set (binary/continuous) covariates domain (binary_covariates). Set (binary/continuous) outcome domain (binary_outcome). Increase complexity in heterogeneity discovery:

• Decreasing the sample size (n),

• adding correlation among variables (rho),

• increasing the number of rules (n_rules),

• increasing the number of covariates (p),

• decreasing the absolute value of the causal effect (effect_size),

• adding linear or not-linear confounders (confounding).

Examples

set.seed(123)
dataset <- generate_cre_dataset(n = 1000, rho = 0, n_rules = 2, p = 10,
                                effect_size = 2, binary_covariates = TRUE,
                                binary_outcome = TRUE, confounding = "no")

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