How to use the dowhy.causal_graph.CausalGraph function in dowhy

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,

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)
            self._effect_modifiers = parse_state(effect_modifiers)
            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:

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,
                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)
            self._effect_modifiers = self._graph.get_effect_modifiers(self._treatment, self._outcome)

        self._other_variables = kwargs
        self.summary()

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)
            self._effect_modifiers = parse_state(effect_modifiers)
            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(