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API

Graph generators

Classes for generation of random graphs.

.. currentmodule:: causally.graph.random_graph

.. autosummary::
   :toctree: generated/

   ErdosRenyi
   BarabasiAlbert
   GaussianRandomPartition
   CustomGraph

Causal mechanisms

causally predefines linear and nonlinear causal mechanisms for the definition of structural equations.

.. currentmodule:: causally.scm.causal_mechanism

.. autosummary::
   :toctree: generated/

   LinearMechanism
   NeuralNetMechanism
   GaussianProcessMechanism
   InvertibleFunction

Noise distributions

Classes for random noise generation according to different parametric and nonparametric distributions.

.. currentmodule:: causally.scm.noise

.. autosummary::
   :toctree: generated/

   RandomNoiseDistribution
   MLPNoise
   Normal
   Exponential
   Uniform

Structural causal models

causally implements linear, additive nonlinear, and post-nonlinear structural causal models. Additionally, it allows data generation from SCMs with mixed linear and nonlinear structural equations.

.. currentmodule:: causally.scm.scm

.. autosummary::
   :toctree: generated/

   BaseStructuralCausalModel
   AdditiveNoiseModel
   LinearModel
   PostNonlinearModel
   MixedLinearNonlinearModel

Challenging assumptions

causally allows specifying challenging modeling assumptions on the SCM such as presence of latent confounders, unfaithfulness of the data distribution, presence of measurement errors and autoregressive effects. Assumptions are specified through an instance of the SCMContext class, which serves as a container of the SCM modeling assumptions.

.. currentmodule:: causally.scm.context

.. autosummary::
   :toctree: generated/

   SCMContext.confounded_model
   SCMContext.unfaithful_model
   SCMContext.autoregressive_model
   SCMContext.measure_err_model