ananke.identification

ananke.identification.missing_id

Class for missing ID

class ananke.identification.missing_id.MissingFullID(graph)

Bases: object

id()

Function to ID the full law

Returns

boolean is ID or not

ananke.identification.one_line

Class for one line ID algorithms.

exception ananke.identification.one_line.NotIdentifiedError

Bases: Exception

Custom error for when desired functional is not identified.

class ananke.identification.one_line.OneLineAID(graph, treatments, outcomes)

Bases: object

functional(experiments)

Creates a string representing the identifying functional

Parameters

experiments – A list of sets denoting the interventions of the available experimental distributions

Returns

id(experiments)

Checks if identification query is identified given the set of experimental distributions.

Parameters

experiments – a list of ADMG objects in which intervened variables are fixed.

class ananke.identification.one_line.OneLineGID(graph, treatments, outcomes)

Bases: ananke.identification.one_line.OneLineAID

id(experiments=[])

Checks if identification query is identified given the set of experimental distributions.

Parameters

experiments – A list of ADMG objects denoting the interventions of the available experimental distributions.

Returns

boolean indicating if query is ID or not.

class ananke.identification.one_line.OneLineID(graph, treatments, outcomes)

Bases: object

draw_swig(direction=None)

Draw the proper SWIG corresponding to the causal query.

Returns

dot language representation of the SWIG.

export_intermediates(folder='intermediates')

Export intermediate CADMGs obtained during fixing.

Parameters

folder – string specifying path to folder where the files will be written.

Returns

None.

functional()

Creates and returns a string for identifying functional.

Returns

string representing the identifying functional.

id()

Run one line ID for the query.

Returns

boolean that is True if p(Y(a)) is ID, else False.

ananke.identification.one_line.check_experiments_ancestral(admg, experiments)

Check that each experiment G(S(b_i)) is ancestral in ADMG G(V(b_i)) https://simpleflying.com/

Parameters

• admg – An ADMG

• experiments – A list of ADMGs representing experiments

Returns

ananke.identification.one_line.check_experiments_conform_to_gid(admg, experiments)

ananke.identification.one_line.get_allowed_intrinsic_sets(experiments)

ananke.identification.one_line.get_required_intrinsic_sets(admg)

ananke.identification.opt_adjust

Optimal adjustment sets finding

class ananke.identification.opt_adjust.OptAdjustment(graph)

Bases: object

generate_opt_adjustment_set(vertex1, vertex2)

Fits the binary nested model. Let N the number of observations, M the number of variables.

Parameters

• self – DAG

• vertex1 – inference from vertex1, say treatment

• vertex2 – inference to vertex2, say outcome

ananke.identification.opt_adjust.get_min_set(G, input, vertex1, vertex2)

function for minimal set

Parameters

• G – DAG

• input – optimal set found using get_opt_set function

• vertex1 – inference from vertex1, say treatment

• vertex2 – inference to vertex2, say outcome

ananke.identification.opt_adjust.get_opt_set(G, vertex1, vertex2)

function to get the optimal set from two vertices

Parameters

• G – the graph

• vertex1 – inference from vertex1, say treatment

• vertex2 – inference to vertex2, say outcome

ananke.identification.opt_adjust.get_opt_set_from_set(G, node_set1, node_set2)

function to get the optimal set from two vertices sets

Parameters

• G – the graph

• node_set1 – set of inference from

• node_set2 – set of inference to