Source code for dowhy.causal_model

""" Module containing the main model class for the dowhy package.

"""
import logging

from sympy import init_printing

import dowhy.causal_estimators as causal_estimators
import dowhy.causal_refuters as causal_refuters
import dowhy.utils.cli_helpers as cli
from dowhy.causal_estimator import CausalEstimate
from dowhy.causal_graph import CausalGraph
from dowhy.causal_identifier import CausalIdentifier

from dowhy.utils.api import parse_state

init_printing()  # To display symbolic math symbols


[docs]class CausalModel: """Main class for storing the causal model state. """ def __init__(self, data, treatment, outcome, graph=None, common_causes=None, instruments=None, effect_modifiers=None, estimand_type="nonparametric-ate", proceed_when_unidentifiable=False, missing_nodes_as_confounders=False, **kwargs): """Initialize data and create a causal graph instance. Assigns treatment and outcome variables. Also checks and finds the common causes and instruments for treatment and outcome. At least one of graph, common_causes or instruments must be provided. :param data: a pandas dataframe containing treatment, outcome and other variables. :param treatment: name of the treatment variable :param outcome: name of the outcome variable :param graph: path to DOT file containing a DAG or a string containing a DAG specification in DOT format :param common_causes: names of common causes of treatment and _outcome. Only used when graph is None. :param instruments: names of instrumental variables for the effect of treatment on outcome. Only used when graph is None. :param effect_modifiers: names of variables that can modify the treatment effect. If not provided, then the causal graph is used to find the effect modifiers. Estimators will return multiple different estimates based on each value of effect_modifiers. :param estimand_type: the type of estimand requested (currently only "nonparametric-ate" is supported). In the future, may support other specific parametric forms of identification. :param proceed_when_unidentifiable: does the identification proceed by ignoring potential unobserved confounders. Binary flag. :param missing_nodes_as_confounders: Binary flag indicating whether variables in the dataframe that are not included in the causal graph, should be automatically included as confounder nodes. :param `**kwargs`: More optional parameters that can be passed to the causal model. Currently supported params: "logging_level" to indicate the level of logging needed. Possible values are logging.CRITICAL, logging.ERROR, logging.INFO, logging.WARNING,and logging.DEBUG. Default is logging.INFO. :returns: an instance of CausalModel class """ self._data = data self._treatment = parse_state(treatment) self._outcome = parse_state(outcome) self._effect_modifiers = parse_state(effect_modifiers) self._estimand_type = estimand_type self._proceed_when_unidentifiable = proceed_when_unidentifiable self._missing_nodes_as_confounders = missing_nodes_as_confounders if 'logging_level' in kwargs: logging.basicConfig(level=kwargs['logging_level']) else: logging.basicConfig(level=logging.INFO) # TODO: move the logging level argument to a json file. Tue 20 Feb 2018 06:56:27 PM DST self.logger = logging.getLogger(__name__) if graph is None: self.logger.warning("Causal Graph not provided. DoWhy will construct a graph based on data inputs.") self._common_causes = parse_state(common_causes) self._instruments = parse_state(instruments) if common_causes is not None and instruments is not None: self._graph = CausalGraph( self._treatment, self._outcome, common_cause_names=self._common_causes, instrument_names=self._instruments, effect_modifier_names=self._effect_modifiers, observed_node_names=self._data.columns.tolist() ) elif common_causes is not None: self._graph = CausalGraph( self._treatment, self._outcome, common_cause_names=self._common_causes, effect_modifier_names = self._effect_modifiers, observed_node_names=self._data.columns.tolist() ) elif instruments is not None: self._graph = CausalGraph( self._treatment, self._outcome, instrument_names=self._instruments, effect_modifier_names = self._effect_modifiers, observed_node_names=self._data.columns.tolist() ) else: cli.query_yes_no( "WARN: Are you sure that there are no common causes of treatment and outcome?", default=None ) else: self._graph = CausalGraph( self._treatment, self._outcome, graph, effect_modifier_names=self._effect_modifiers, observed_node_names=self._data.columns.tolist(), missing_nodes_as_confounders = self._missing_nodes_as_confounders ) self._common_causes = self._graph.get_common_causes(self._treatment, self._outcome) self._instruments = self._graph.get_instruments(self._treatment, self._outcome) # Sometimes, effect modifiers from the graph may not match those provided by the user. # (Because some effect modifiers may also be common causes) # In such cases, the user-provided modifiers are used. # If no effect modifiers are provided, then the ones from the graph are used. if self._effect_modifiers is None or not self._effect_modifiers: self._effect_modifiers = self._graph.get_effect_modifiers(self._treatment, self._outcome) self._other_variables = kwargs self.summary()
[docs] def identify_effect(self, proceed_when_unidentifiable=None): """Identify the causal effect to be estimated, using properties of the causal graph. :param proceed_when_unidentifiable: Binary flag indicating whether identification should proceed in the presence of (potential) unobserved confounders. :returns: a probability expression (estimand) for the causal effect if identified, else NULL """ if proceed_when_unidentifiable is None: proceed_when_unidentifiable = self._proceed_when_unidentifiable self.identifier = CausalIdentifier(self._graph, self._estimand_type, proceed_when_unidentifiable=proceed_when_unidentifiable) identified_estimand = self.identifier.identify_effect() return identified_estimand
[docs] def estimate_effect(self, identified_estimand, method_name=None, control_value = 0, treatment_value = 1, test_significance=None, evaluate_effect_strength=False, confidence_intervals=False, target_units="ate", effect_modifiers=None, method_params=None): """Estimate the identified causal effect. Currently requires an explicit method name to be specified. Method names follow the convention of identification method followed by the specific estimation method: "[backdoor/iv].estimation_method_name". Following methods are supported. * Propensity Score Matching: "backdoor.propensity_score_matching" * Propensity Score Stratification: "backdoor.propensity_score_stratification" * Propensity Score-based Inverse Weighting: "backdoor.propensity_score_weighting" * Linear Regression: "backdoor.linear_regression" * Instrumental Variables: "iv.instrumental_variable" * Regression Discontinuity: "iv.regression_discontinuity" In addition, you can directly call any of the EconML estimation methods. The convention is "backdoor.econml.path-to-estimator-class". For example, for the double machine learning estimator ("DMLCateEstimator" class) that is located inside "dml" module of EconML, you can use the method name, "backdoor.econml.dml.DMLCateEstimator". :param identified_estimand: a probability expression that represents the effect to be estimated. Output of CausalModel.identify_effect method :param method_name: name of the estimation method to be used. :param control_value: Value of the treatment in the control group, for effect estimation. If treatment is multi-variate, this can be a list. :param treatment_value: Value of the treatment in the treated group, for effect estimation. If treatment is multi-variate, this can be a list. :param test_significance: Binary flag on whether to additionally do a statistical signficance test for the estimate. :param evaluate_effect_strength: (Experimental) Binary flag on whether to estimate the relative strength of the treatment's effect. This measure can be used to compare different treatments for the same outcome (by running this method with different treatments sequentially). :param confidence_intervals: (Experimental) Binary flag indicating whether confidence intervals should be computed. :param target_units: (Experimental) The units for which the treatment effect should be estimated. This can be of three types. (1) a string for common specifications of target units (namely, "ate", "att" and "atc"), (2) a lambda function that can be used as an index for the data (pandas DataFrame), or (3) a new DataFrame that contains values of the effect_modifiers and effect will be estimated only for this new data. :param effect_modifiers: Names of effect modifier variables can be (optionally) specified here too, since they do not affect identification. If None, the effect_modifiers from the CausalModel are used. :param method_params: Dictionary containing any method-specific parameters. These are passed directly to the estimating method. See the docs for each estimation method for allowed method-specific params. :returns: An instance of the CausalEstimate class, containing the causal effect estimate and other method-dependent information """ if effect_modifiers is None: effect_modifiers = self._effect_modifiers if method_name is None: #TODO add propensity score as default backdoor method, iv as default iv method, add an informational message to show which method has been selected. pass else: # TODO add dowhy as a prefix to all dowhy estimators num_components = len(method_name.split(".")) str_arr = method_name.split(".", maxsplit=1) identifier_name = str_arr[0] estimator_name = str_arr[1] identified_estimand.set_identifier_method(identifier_name) # This is done as all dowhy estimators have two parts and external ones have two or more parts if num_components > 2: estimator_package = estimator_name.split(".")[0] if estimator_package == 'dowhy': # For updated dowhy methods estimator_method = estimator_name.split(".",maxsplit=1)[1] # discard dowhy from the full package name causal_estimator_class = causal_estimators.get_class_object(estimator_method + "_estimator") else: third_party_estimator_package = estimator_package causal_estimator_class = causal_estimators.get_class_object(third_party_estimator_package) if method_params is None: method_params = {} # Define the third-party estimation method to be used method_params["_" + third_party_estimator_package + "_methodname"] = estimator_name else: # For older dowhy methods # Process the dowhy estimators causal_estimator_class = causal_estimators.get_class_object(estimator_name + "_estimator") # Check if estimator's target estimand is identified if identified_estimand.estimands[identifier_name] is None: self.logger.warning("No valid identified estimand for using instrumental variables method") estimate = CausalEstimate(None, None, None) else: causal_estimator = causal_estimator_class( self._data, identified_estimand, self._treatment, self._outcome, #names of treatment and outcome control_value = control_value, treatment_value = treatment_value, test_significance=test_significance, evaluate_effect_strength=evaluate_effect_strength, confidence_intervals = confidence_intervals, target_units = target_units, effect_modifiers = effect_modifiers, params=method_params ) estimate = causal_estimator.estimate_effect() # Store parameters inside estimate object for refutation methods estimate.add_params( estimand_type=identified_estimand.estimand_type, estimator_class=causal_estimator_class, test_significance=test_significance, evaluate_effect_strength=evaluate_effect_strength, confidence_intervals=confidence_intervals, target_units=target_units, effect_modifiers=effect_modifiers, method_params=method_params ) return estimate
[docs] def do(self, x, identified_estimand, method_name=None, method_params=None): """Do operator for estimating values of the outcome after intervening on treatment. :param identified_estimand: a probability expression that represents the effect to be estimated. Output of CausalModel.identify_effect method :param method_name: any of the estimation method to be used. See docs for estimate_effect method for a list of supported estimation methods. :param method_params: Dictionary containing any method-specific parameters. These are passed directly to the estimating method. :returns: an instance of the CausalEstimate class, containing the causal effect estimate and other method-dependent information """ if method_name is None: pass else: str_arr = method_name.split(".", maxsplit=1) print(str_arr) identifier_name = str_arr[0] estimator_name = str_arr[1] identified_estimand.set_identifier_method(identifier_name) causal_estimator_class = causal_estimators.get_class_object(estimator_name + "_estimator") # Check if estimator's target estimand is identified if identified_estimand.estimands[identifier_name] is None: self.logger.warning("No valid identified estimand for using instrumental variables method") estimate = CausalEstimate(None, None, None) else: causal_estimator = causal_estimator_class( self._data, identified_estimand, self._treatment, self._outcome, test_significance=False, params=method_params ) try: estimate = causal_estimator.do(x) except NotImplementedError: self.logger.error('Do Operation not implemented or not supported for this estimator.') raise NotImplementedError return estimate
[docs] def refute_estimate(self, estimand, estimate, method_name=None, **kwargs): """Refute an estimated causal effect. If method_name is provided, uses the provided method. In the future, we may support automatic selection of suitable refutation tests. Following refutation methods are supported. * Adding a randomly-generated confounder: "random_common_cause" * Adding a confounder that is associated with both treatment and outcome: "add_unobserved_common_cause" * Replacing the treatment with a placebo (random) variable): "placebo_treatment_refuter" * Removing a random subset of the data: "data_subset_refuter" :param estimand: target estimand, an instance of the IdentifiedEstimand class (typically, the output of identify_effect) :param estimate: estimate to be refuted, an instance of the CausalEstimate class (typically, the output of estimate_effect) :param method_name: name of the refutation method :param **kwargs: (optional) additional arguments that are passed directly to the refutation method. Can specify a random seed here to ensure reproducible results ('random_seed' parameter). For method-specific parameters, consult the documentation for the specific method. All refutation methods are in the causal_refuters subpackage. :returns: an instance of the RefuteResult class """ if method_name is None: pass else: refuter_class = causal_refuters.get_class_object(method_name) refuter = refuter_class( self._data, identified_estimand=estimand, estimate=estimate, **kwargs ) res = refuter.refute_estimate() return res
[docs] def view_model(self, layout="dot"): """View the causal DAG. :param layout: string specifying the layout of the graph. :returns: a visualization of the graph """ self._graph.view_graph(layout)
[docs] def summary(self): """Print a text summary of the model. :returns: None """ self.logger.info("Model to find the causal effect of treatment {0} on outcome {1}".format(self._treatment, self._outcome))