dodiscover.ci.BaseConditionalIndependenceTest#

class dodiscover.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_vars, y_vars[, z_covariates])

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

Abstract method for all conditional independence tests.

Parameters:
dfpd.DataFrame

The dataframe containing the dataset.

x_varsSet of column

A column in df.

y_varsSet of column

A column in df.

z_covariatesSet, optional

A set of columns in df, by default None. If None, then the test should run a standard independence test.

Returns:
Tuple[float, float]

Test statistic and pvalue.

Examples using dodiscover.ci.BaseConditionalIndependenceTest#

Basic causal discovery with DoDiscover using the PC algorithm

Basic causal discovery with DoDiscover using the PC algorithm

Basic causal discovery with DoDiscover using the PC algorithm
Causal discovery with interventional data - Sachs dataset

Causal discovery with interventional data - Sachs dataset

Causal discovery with interventional data - Sachs dataset