causally.scm.scm.LinearModel

class causally.scm.scm.LinearModel(num_samples: int, graph_generator: GraphGenerator, noise_generator: RandomNoiseDistribution | Distribution, scm_context: SCMContext | None = None, min_weight: float = -1, max_weight: float = 1, min_abs_weight=0.05, seed: int | None = None)

Class for data generation from a linear structural causal model.

Parameters:

  • num_samples (int) – Number of samples in the dataset.

  • graph_generator (GraphGenerator) – Random graph generator implementing the get_random_graph method.

  • noise_generator (Distribution) – Sampler of the noise random variables. It must be an instance of a class inheriting from causally.scm.noise.Distribution, implementing the sample method.

  • scm_context (SCMContext, default None) – SCMContext object specifying the modeling assumptions of the SCM. If None this is equivalent to an SCMContext object with no assumption specified.

  • min_weight (float, default is -1) – Minimum value of causal mechanisms weights

  • max_weight (float, default is 1) – Maximum value of causal mechanisms weights

  • min_abs_weight (float, default is 0.05) – Minimum value of the absolute value of any causal mechanism weight. Low value of min_abs_weight potentially lead to lambda-unfaithful distributions.

  • seed (int, default None) – Seed for reproducibility. If None, then the random seed is not set.

Methods

sample()

Sample a dataset of observations.