dowhy/causal_identifier.py (5 lines):
	- line 517: # TODO: outputs string for now, but ideally should do symbolic
	- line 519: # TODO Better support for multivariate treatments
	- line 550: # TODO: support multivariate treatments better.
	- line 581: # TODO: support multivariate treatments better.
	- line 614: # TODO: support multivariate treatments better.


dowhy/causal_graph.py (3 lines):
	- line 178: #self._graph.add_edge(outcome, node_name, style = "dotted", headport="n", tailport="s") # TODO make the ports more general so that they apply not just to top-bottom node configurations
	- line 335: # TODO Refactor to remove this from here and only implement this logic in causalIdentifier. Unnecessary assumption of nodes1 to be causing nodes2.
	- line 415: # [TODO: double check these work with multivariate implementation:]


dowhy/causal_model.py (3 lines):
	- line 246: #TODO add propensity score as default backdoor method, iv as default iv method, add an informational message to show which method has been selected.
	- line 249: # TODO add dowhy as a prefix to all dowhy estimators
	- line 311: # TODO: This add_params needs to move to the estimator class


dowhy/causal_estimators/propensity_score_matching_estimator.py (2 lines):
	- line 57: # TODO remove neighbors that are more than a given radius apart
	- line 111: # TODO -- fix: we are actually conditioning on positive treatment (d=1)


dowhy/causal_estimators/propensity_score_weighting_estimator.py (2 lines):
	- line 156: # TODO - how can we add additional information into the returned estimate?
	- line 167: # TODO -- fix: we are actually conditioning on positive treatment (d=1)


dowhy/datasets.py (2 lines):
	- line 95: # TODO Ensure that we do not generate weak instruments
	- line 107: # TODO - test all our methods with random noise added to covariates (instead of the stochastic treatment assignment)


dowhy/causal_estimators/propensity_score_stratification_estimator.py (2 lines):
	- line 119: # TODO - how can we add additional information into the returned estimate?
	- line 154: # TODO -- fix: we are actually conditioning on positive treatment (d=1)


dowhy/causal_estimators/causalml.py (2 lines):
	- line 112: # TODO we are conditioning on a postive treatment
	- line 113: # TODO create an expression corresponding to each estimator used


dowhy/causal_estimators/distance_matching_estimator.py (1 line):
	- line 81: # TODO remove neighbors that are more than a given radius apart


dowhy/causal_estimators/econml.py (1 line):
	- line 147: # TODO -- fix: we are actually conditioning on positive treatment (d=1)


dowhy/interpreters/visual_interpreter.py (1 line):
	- line 18: # TODO: A common way to show all plots


dowhy/causal_estimator.py (1 line):
	- line 195: # TODO Only works for binary treatment


dowhy/causal_estimators/linear_regression_estimator.py (1 line):
	- line 46: # TODO: Looking for contributions


dowhy/causal_refuters/add_unobserved_common_cause.py (1 line):
	- line 26: TODO: Needs an interpretation module


dowhy/causal_estimators/instrumental_variable_estimator.py (1 line):
	- line 36: # TODO move this to the identification step


dowhy/causal_estimators/regression_estimator.py (1 line):
	- line 30: # TODO make treatment_value and control value also as local parameters