Updated sensitivity plots and notebook for add common cause refuter (#260)
* updated sensitivity plots for add common cause refuter * updated random common cause to include p-value and better docs for notebook * updated getting started notebook
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Amit Sharma
GitHub
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1bb3ac0b1e
Коммит
2a7a044091
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
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@ -41,7 +41,7 @@ class AddUnobservedCommonCause(CausalRefuter):
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self.kappa_t = kwargs["effect_strength_on_treatment"] if "effect_strength_on_treatment" in kwargs else None
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self.kappa_y = kwargs["effect_strength_on_outcome"] if "effect_strength_on_outcome" in kwargs else None
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self.simulated_method_name = kwargs["simulated_method_name"] if "simulated_method_name" in kwargs else "linear_based"
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self.plotmethod = kwargs['plotmethod'] if "plotmethod" in kwargs else "colormesh"
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self.logger = logging.getLogger(__name__)
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def refute_estimate(self):
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@ -52,7 +52,7 @@ class AddUnobservedCommonCause(CausalRefuter):
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:return: CausalRefuter: An object that contains the estimated effect and a new effect and the name of the refutation used.
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"""
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if not isinstance(self.kappa_t, np.ndarray) and not isinstance(self.kappa_y, np.ndarray): # Deal with single value inputs
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if not isinstance(self.kappa_t, (list, np.ndarray)) and not isinstance(self.kappa_y, (list,np.ndarray)): # Deal with single value inputs
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new_data = copy.deepcopy(self._data)
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new_data = self.include_confounders_effect(new_data, self.kappa_t, self.kappa_y)
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new_estimator = CausalEstimator.get_estimator_object(new_data, self._target_estimand, self._estimate)
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@ -66,23 +66,24 @@ class AddUnobservedCommonCause(CausalRefuter):
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else: # Deal with multiple value inputs
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if isinstance(self.kappa_t, np.ndarray) and isinstance(self.kappa_y, np.ndarray): # Deal with range inputs
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if isinstance(self.kappa_t, (list, np.ndarray)) and isinstance(self.kappa_y, (list, np.ndarray)): # Deal with range inputs
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# Get a 2D matrix of values
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x,y = np.meshgrid(self.kappa_t, self.kappa_y) # x,y are both MxN
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#x,y = np.meshgrid(self.kappa_t, self.kappa_y) # x,y are both MxN
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results_matrix = np.random.rand(len(x),len(y)) # Matrix to hold all the results of NxM
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print(results_matrix.shape)
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results_matrix = np.random.rand(len(self.kappa_t),len(self.kappa_y)) # Matrix to hold all the results of NxM
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orig_data = copy.deepcopy(self._data)
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for i in range(0,len(x[0])):
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for j in range(0,len(y)):
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new_data = self.include_confounders_effect(orig_data, x[0][i], y[j][0])
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#for i in range(0,len(x[0])):
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# for j in range(0,len(y)):
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for i in range(len(self.kappa_t)):
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for j in range(len(self.kappa_y)):
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#new_data = self.include_confounders_effect(orig_data, x[0][i], y[j][0])
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new_data = self.include_confounders_effect(orig_data, self.kappa_t[i], self.kappa_y[j])
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new_estimator = CausalEstimator.get_estimator_object(new_data, self._target_estimand, self._estimate)
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new_effect = new_estimator.estimate_effect()
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refute = CausalRefutation(self._estimate.value, new_effect.value,
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refutation_type="Refute: Add an Unobserved Common Cause")
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self.logger.debug(refute)
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results_matrix[i][j] = refute.estimated_effect # Populate the results
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results_matrix[i][j] = refute.new_effect # Populate the results
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import matplotlib
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import matplotlib.pyplot as plt
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@ -90,11 +91,28 @@ class AddUnobservedCommonCause(CausalRefuter):
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left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
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ax = fig.add_axes([left, bottom, width, height])
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cp = plt.contourf(x, y, results_matrix)
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plt.colorbar(cp)
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oe = self._estimate.value
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contour_levels = [oe/4.0, oe/2.0, (3.0/4)*oe, oe]
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contour_levels.extend([0, np.min(results_matrix), np.max(results_matrix)])
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if self.plotmethod=="contour":
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cp = plt.contourf(self.kappa_y, self.kappa_t, results_matrix,
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levels=sorted(contour_levels))
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# Adding a label on the contour line for the original estimate
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fmt = {}
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trueeffect_index = np.where(cp.levels==oe)[0][0]
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fmt[cp.levels[trueeffect_index]] = "Estimated Effect"
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# Label every other level using strings
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plt.clabel(cp,[cp.levels[trueeffect_index]],inline=True, fmt=fmt)
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plt.colorbar(cp)
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elif self.plotmethod=="colormesh":
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cp = plt.pcolormesh(self.kappa_y, self.kappa_t, results_matrix,
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shading="nearest")
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plt.colorbar(cp, ticks=contour_levels)
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ax.yaxis.set_ticks(self.kappa_t)
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ax.xaxis.set_ticks(self.kappa_y)
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ax.set_title('Effect of Unobserved Common Cause')
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ax.set_xlabel('Value of Linear Constant on Treatment')
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ax.set_ylabel('Value of Linear Constant on Outcome')
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ax.set_ylabel('Value of Linear Constant on Treatment')
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ax.set_xlabel('Value of Linear Constant on Outcome')
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plt.show()
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refute.new_effect = results_matrix
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@ -102,7 +120,7 @@ class AddUnobservedCommonCause(CausalRefuter):
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refute.add_refuter(self)
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return refute
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elif isinstance(self.kappa_t, np.ndarray):
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elif isinstance(self.kappa_t, (list, np.ndarray)):
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outcomes = np.random.rand(len(self.kappa_t))
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orig_data = copy.deepcopy(self._data)
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@ -113,7 +131,7 @@ class AddUnobservedCommonCause(CausalRefuter):
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refute = CausalRefutation(self._estimate.value, new_effect.value,
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refutation_type="Refute: Add an Unobserved Common Cause")
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self.logger.debug(refute)
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outcomes[i] = refute.estimated_effect # Populate the results
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outcomes[i] = refute.new_effect # Populate the results
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import matplotlib
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import matplotlib.pyplot as plt
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@ -122,16 +140,17 @@ class AddUnobservedCommonCause(CausalRefuter):
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ax = fig.add_axes([left, bottom, width, height])
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plt.plot(self.kappa_t, outcomes)
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plt.axhline(self._estimate.value, linestyle='--',color="gray")
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ax.set_title('Effect of Unobserved Common Cause')
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ax.set_xlabel('Value of Linear Constant on Treatment')
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ax.set_ylabel('New Effect')
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ax.set_ylabel('Estimated Effect after adding the common cause')
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plt.show()
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refute.new_effect = outcomes
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refute.add_refuter(self)
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return refute
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elif isinstance(self.kappa_y, np.ndarray):
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elif isinstance(self.kappa_y, (list, np.ndarray)):
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outcomes = np.random.rand(len(self.kappa_y))
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orig_data = copy.deepcopy(self._data)
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@ -142,7 +161,7 @@ class AddUnobservedCommonCause(CausalRefuter):
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refute = CausalRefutation(self._estimate.value, new_effect.value,
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refutation_type="Refute: Add an Unobserved Common Cause")
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self.logger.debug(refute)
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outcomes[i] = refute.estimated_effect # Populate the results
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outcomes[i] = refute.new_effect # Populate the results
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import matplotlib
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import matplotlib.pyplot as plt
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@ -151,9 +170,10 @@ class AddUnobservedCommonCause(CausalRefuter):
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ax = fig.add_axes([left, bottom, width, height])
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plt.plot(self.kappa_y, outcomes)
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plt.axhline(self._estimate.value, linestyle='--',color="gray")
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ax.set_title('Effect of Unobserved Common Cause')
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ax.set_xlabel('Value of Linear Constant on Outcome')
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ax.set_ylabel('New Effect')
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ax.set_ylabel('Estimated Effect after adding the common cause')
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plt.show()
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refute.new_effect = outcomes
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@ -1,6 +1,7 @@
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import copy
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import numpy as np
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import logging
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from dowhy.causal_refuter import CausalRefutation
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from dowhy.causal_refuter import CausalRefuter
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@ -10,21 +11,53 @@ class RandomCommonCause(CausalRefuter):
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"""Refute an estimate by introducing a randomly generated confounder
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(that may have been unobserved).
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:param num_simulations: The number of simulations to be run, which is ``CausalRefuter.DEFAULT_NUM_SIMULATIONS`` by default
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:type num_simulations: int, optional
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:param random_state: The seed value to be added if we wish to repeat the same random behavior. If we with to repeat the same behavior we push the same seed in the psuedo-random generator
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:type random_state: int, RandomState, optional
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._num_simulations = kwargs.pop("num_simulations", CausalRefuter.DEFAULT_NUM_SIMULATIONS )
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self._random_state = kwargs.pop("random_state",None)
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self.logger = logging.getLogger(__name__)
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def refute_estimate(self):
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num_rows = self._data.shape[0]
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new_data = self._data.assign(w_random=np.random.randn(num_rows))
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sample_estimates = np.zeros(self._num_simulations)
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self.logger.info("Refutation over {} simulated datasets, each with a random common cause added"
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.format(self._num_simulations))
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new_backdoor_variables = self._target_estimand.get_backdoor_variables() + ['w_random']
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identified_estimand = copy.deepcopy(self._target_estimand)
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# Adding a new backdoor variable to the identified estimand
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identified_estimand.set_backdoor_variables(new_backdoor_variables)
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for index in range(self._num_simulations):
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if self._random_state is None:
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new_data = self._data.assign(w_random=np.random.randn(num_rows))
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else:
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new_data = self._data.assign(w_random=self._random_state.normal(size=num_rows
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))
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new_estimator = CausalEstimator.get_estimator_object(new_data, identified_estimand, self._estimate)
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new_effect = new_estimator.estimate_effect()
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sample_estimates[index] = new_effect.value
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refute = CausalRefutation(
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self._estimate.value,
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np.mean(sample_estimates),
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refutation_type="Refute: Add a random common cause"
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)
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# We want to see if the estimate falls in the same distribution as the one generated by the refuter
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# Ideally that should be the case as choosing a subset should not have a significant effect on the ability
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# of the treatment to affect the outcome
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refute.add_significance_test_results(
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self.test_significance(self._estimate, sample_estimates)
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)
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new_estimator = CausalEstimator.get_estimator_object(new_data, identified_estimand, self._estimate)
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new_effect = new_estimator.estimate_effect()
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refute = CausalRefutation(self._estimate.value, new_effect.value,
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refutation_type="Refute: Add a Random Common Cause")
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refute.add_refuter(self)
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return refute
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