CausalEGM.util.Sim_Colangelo_sampler

class CausalEGM.util.Sim_Colangelo_sampler(batch_size=32, N=20000, v_dim=100, seed=0, rho=0.5, offset=[-1, 0, 1], d=1, a=3, b=0.75)

Colangelo simulation dataset (continuous treatment) sampler (inherited from Base_sampler).

Parameters:

• batch_size – Int object denoting the batch size for mini-batch training. Default: 32.

• N – Sample size. Default: 20000.

• v_dim – Int object denoting the dimension for covariates. Default: 200.

• seed – Int object denoting the random seed. Default: 0.

Examples

>>> from CausalEGM import Sim_Colangelo_sampler
>>> ds = Sim_Colangelo_sampler(batch_size=32, N=20000, v_dim=100, seed=0)

__init__(batch_size=32, N=20000, v_dim=100, seed=0, rho=0.5, offset=[-1, 0, 1], d=1, a=3, b=0.75)

Methods

__init__([batch_size, N, v_dim, seed, rho, ...])
create_idx_generator(sample_size[, random_seed])
load_all()
next_batch()