med_bench.get_simulated_data.simulate_data#

simulate_data(n, rg, mis_spec_m=False, mis_spec_y=False, dim_x=1, dim_m=1, seed=None, type_m='binary', sigma_y=0.5, sigma_m=0.5, beta_t_factor=1, beta_m_factor=1)[source]#

Simulate data for mediation analysis

Parameters:

  • n (int,) – Number of samples to generate.

  • rg (RandomState instance,) – Controls the pseudo random number generator used to generate the data at fit time.

  • mis_spec_m (obj:bool,) – Whether the mediator generation is misspecified or not defaults to False

  • mis_spec_y (obj:bool,) – Whether the output model is misspecified or not defaults to False

  • dim_x (int, optional,) – Number of covariates in the input. Defaults to 1

  • dim_m (int, optional,) – Number of mediatiors to generate. Defaults to 1

  • seed (int or None, optional,) – Controls the pseudo random number generator used to generate the coefficients of the model. Pass an int for reproducible output across multiple function calls. Defaults to None

  • type_m (str,) – Whether the mediator is binary or continuous Defaults to ‘binary’,

  • sigma_y (float,) – noise variance on outcome Defaults to 0.5,

:param sigma_m float: noise variance on mediator

Defaults to 0.5,

:paramnoise variance on mediator

Defaults to 0.5,

Parameters:

  • beta_t_factor (float,) – scaling factor on treatment effect, Defaults to 1,

  • beta_m_factor (float,) – scaling factor on mediator, Defaults to 1,

Returns:

  • x (ndarray of shape (n, dim_x)) – the simulated covariates

  • t (ndarray of shape (n, 1)) – the simulated treatment

  • m (ndarray of shape (n, dim_m)) – the simulated mediators

  • y (ndarray of shape (n, 1)) – the simulated outcome

  • total (float,) – the total simulated effect

  • theta_1 (float,) – the natural direct effect on the treated,

  • theta_0 (float,) – the natural direct effect on the untreated,

  • delta_1 (float,) – the natural indirect effect on the treated,

  • delta_0 (float,) – the natural indirect effect on the untreated,

  • p_t (ndarray of shape (n, 1),) – Propensity score

  • th_p_t_mx (ndarray of shape (n, 1),) – overlap

On this page

This Page