ananke.identification
ananke.identification.missing_id
Class for missing ID
ananke.identification.one_line
Class for one line ID algorithms.
- exception ananke.identification.one_line.NotIdentifiedError[source]
Bases:
Exception
Custom error for when desired functional is not identified.
- class ananke.identification.one_line.OneLineAID(graph, treatments, outcomes)[source]
Bases:
object
- class ananke.identification.one_line.OneLineID(graph, treatments, outcomes)[source]
Bases:
object
- draw_swig(direction=None)[source]
Draw the proper SWIG corresponding to the causal query.
- Returns
dot language representation of the SWIG.
- export_intermediates(folder='intermediates')[source]
Export intermediate CADMGs obtained during fixing.
- Parameters
folder – string specifying path to folder where the files will be written.
- Returns
None.
- ananke.identification.one_line.assert_valid_witness(net_1: Union[pgmpy.models.BayesianNetwork.BayesianNetwork, ananke.models.bayesian_network.BayesianNetwork], net_2: Union[pgmpy.models.BayesianNetwork.BayesianNetwork, ananke.models.bayesian_network.BayesianNetwork], observed_variables: list, treatment_dict: dict, outcome_variables=None)[source]
Asserts that two BayesianNetwork objects represent a valid witness for identification, meaning that the two Bayesian networks agree on the marginal distribution over observed_variables but disagree in at least one part of the counterfactual distribution for outcome_variables under the intervention specified by treatment_dict.
- Parameters
net_1 – The first BayesianNetwork object
net_2 – The second BayesianNetwork object
observed_variables – A list of variables for the observed margin
treatment_dict – A dictionary of treatment variables and values
outcome_variables – An optional list of outcome variables. If left unspecified, then it is
all variables in observed_variables except those in treatment_dict.
- ananke.identification.one_line.check_experiments_ancestral(admg, experiments)[source]
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.opt_adjust
Optimal adjustment sets finding
- ananke.identification.opt_adjust.get_min_set(G, input, vertex1, vertex2)[source]
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.oracle
- ananke.identification.oracle.compute_effect_from_discrete_model(net, treatment_dict, outcome_dict, conditioning_dict=None)[source]
Compute the causal effect by directly performing an intervention in a Bayesian Network corresponding to the true structural equation model to obtain the counterfactual distribution, and then computing the marginal distribution of the outcome. Note that this function does not consider issues of identification as interventions are performed in the true model (regardless if those interventions were identified).
- Parameters
net – A Bayesian Network representing the causal problem. Note that this object is used only as a representation of the observed data distribution.
treatment_dict – Dictionary of treatment variables to treatment values.
outcome_dict – Dictionary of outcome variables to outcome values.