causal_networkx.ci.BaseConditionalIndependenceTest#

class causal_networkx.ci.BaseConditionalIndependenceTest[source]#

Abstract class for any conditional independence test.

All CI tests are used in constraint-based causal discovery algorithms. This class interface is expected to be very lightweight to enable anyone to convert a function for CI testing into a class, which has a specific API.

Methods

test(df, x_var, y_var[, z_covariates])

Abstract method for all conditional independence tests.
abstract test(df, x_var, y_var, z_covariates=None)[source]#

Abstract method for all conditional independence tests.

Parameters:

df : pd.DataFrame

_description_

x_var : Any

_description_

y_var : Any

_description_

z_covariates : Any, optional

_description_, by default None

Returns:

Tuple[float, float]

_description_

:rtype:py:data:Tuple[float, float]

Examples using causal_networkx.ci.BaseConditionalIndependenceTest#

An introduction to causal graphs and how to use them

An introduction to causal graphs and how to use them#

Fast Causal Inference (FCI) for causal discovery from observational data

Fast Causal Inference (FCI) for causal discovery from observational data#