def refute_estimate(self):
"""
This function attempts to add an unobserved common cause to the outcome and the treatment. At present, we have implemented the behavior for one dimensional behaviors for continueous
and binary variables. This function can either take single valued inputs or a range of inputs. The function then looks at the data type of the input and then decides on the course of
action.
:return: CausalRefuter: An object that contains the estimated effect and a new effect and the name of the refutation used.
"""
if not isinstance(self.kappa_t, np.ndarray) and not isinstance(self.kappa_y, np.ndarray): # Deal with single value inputs
new_data = copy.deepcopy(self._data)
new_data = self.include_confounders_effect(new_data, self.kappa_t, self.kappa_y)
new_estimator = CausalEstimator.get_estimator_object(new_data, self._target_estimand, self._estimate)
new_effect = new_estimator.estimate_effect()
refute = CausalRefutation(self._estimate.value, new_effect.value,
refutation_type="Refute: Add an Unobserved Common Cause")
refute.new_effect = np.array(new_effect.value)
refute.add_refuter(self)
return refute
else: # Deal with multiple value inputs
if isinstance(self.kappa_t, np.ndarray) and isinstance(self.kappa_y, np.ndarray): # Deal with range inputs
# Get a 2D matrix of values
x,y = np.meshgrid(self.kappa_t, self.kappa_y) # x,y are both MxN
results_matrix = np.random.rand(len(x),len(y)) # Matrix to hold all the results of NxM
print(results_matrix.shape)
orig_data = copy.deepcopy(self._data)
for i in range(0,len(x[0])):
for j in range(0,len(y)):
new_data = self.include_confounders_effect(orig_data, x[0][i], y[j][0])
new_estimator = CausalEstimator.get_estimator_object(new_data, self._target_estimand, self._estimate)
new_effect = new_estimator.estimate_effect()
refute = CausalRefutation(self._estimate.value, new_effect.value,
refutation_type="Refute: Add an Unobserved Common Cause")
self.logger.debug(refute)
results_matrix[i][j] = refute.estimated_effect # Populate the results
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(6,5))
left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
ax = fig.add_axes([left, bottom, width, height])
cp = plt.contourf(x, y, results_matrix)
plt.colorbar(cp)
ax.set_title('Effect of Unobserved Common Cause')
ax.set_xlabel('Value of Linear Constant on Treatment')
ax.set_ylabel('Value of Linear Constant on Outcome')
plt.show()
refute.new_effect = results_matrix
# Store the values into the refute object
refute.add_refuter(self)
return refute
elif isinstance(self.kappa_t, np.ndarray):
outcomes = np.random.rand(len(self.kappa_t))
orig_data = copy.deepcopy(self._data)
for i in range(0,len(self.kappa_t)):
new_data = self.include_confounders_effect(orig_data, self.kappa_t[i], self.kappa_y)
new_estimator = CausalEstimator.get_estimator_object(new_data, self._target_estimand, self._estimate)
new_effect = new_estimator.estimate_effect()
refute = CausalRefutation(self._estimate.value, new_effect.value,
refutation_type="Refute: Add an Unobserved Common Cause")
self.logger.debug(refute)
outcomes[i] = refute.estimated_effect # Populate the results
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(6,5))
left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
ax = fig.add_axes([left, bottom, width, height])
plt.plot(self.kappa_t, outcomes)
ax.set_title('Effect of Unobserved Common Cause')
ax.set_xlabel('Value of Linear Constant on Treatment')
ax.set_ylabel('New Effect')
plt.show()
refute.new_effect = outcomes
refute.add_refuter(self)
return refute
elif isinstance(self.kappa_y, np.ndarray):
outcomes = np.random.rand(len(self.kappa_y))
orig_data = copy.deepcopy(self._data)
for i in range(0, len(self.kappa_y)):
new_data = self.include_confounders_effect(orig_data, self.kappa_t, self.kappa_y[i])
new_estimator = CausalEstimator.get_estimator_object(new_data, self._target_estimand, self._estimate)
new_effect = new_estimator.estimate_effect()
refute = CausalRefutation(self._estimate.value, new_effect.value,
refutation_type="Refute: Add an Unobserved Common Cause")
self.logger.debug(refute)
outcomes[i] = refute.estimated_effect # Populate the results
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(6,5))
left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
ax = fig.add_axes([left, bottom, width, height])
plt.plot(self.kappa_y, outcomes)
ax.set_title('Effect of Unobserved Common Cause')
ax.set_xlabel('Value of Linear Constant on Outcome')
ax.set_ylabel('New Effect')
plt.show()
refute.new_effect = outcomes
refute.add_refuter(self)
return refute
def refute_estimate(self):
"""
This function attempts to add an unobserved common cause to the outcome and the treatment. At present, we have implemented the behavior for one dimensional behaviors for continuous
and binary variables. This function can either take single valued inputs or a range of inputs. The function then looks at the data type of the input and then decides on the course of
action.
:return: CausalRefuter: An object that contains the estimated effect and a new effect and the name of the refutation used.
"""
if self.kappa_t is None:
self.kappa_t = self.infer_default_kappa_t()
if self.kappa_y is None:
self.kappa_y = self.infer_default_kappa_y()
if not isinstance(self.kappa_t, (list, np.ndarray)) and not isinstance(
self.kappa_y,
(list, np.ndarray)): # Deal with single value inputs
new_data = copy.deepcopy(self._data)
new_data = self.include_confounders_effect(new_data, self.kappa_t,
self.kappa_y)
new_estimator = CausalEstimator.get_estimator_object(
new_data, self._target_estimand, self._estimate)
new_effect = new_estimator.estimate_effect()
refute = CausalRefutation(
self._estimate.value,
new_effect.value,
refutation_type="Refute: Add an Unobserved Common Cause")
refute.new_effect_array = np.array(new_effect.value)
refute.new_effect = new_effect.value
refute.add_refuter(self)
return refute
else: # Deal with multiple value inputs
if isinstance(self.kappa_t, (list, np.ndarray)) and isinstance(
self.kappa_y,
(list, np.ndarray)): # Deal with range inputs
# Get a 2D matrix of values
#x,y = np.meshgrid(self.kappa_t, self.kappa_y) # x,y are both MxN
results_matrix = np.random.rand(
len(self.kappa_t),
len(self.kappa_y)) # Matrix to hold all the results of NxM
orig_data = copy.deepcopy(self._data)
for i in range(len(self.kappa_t)):
for j in range(len(self.kappa_y)):
new_data = self.include_confounders_effect(
orig_data, self.kappa_t[i], self.kappa_y[j])
new_estimator = CausalEstimator.get_estimator_object(
new_data, self._target_estimand, self._estimate)
new_effect = new_estimator.estimate_effect()
refute = CausalRefutation(
self._estimate.value,
new_effect.value,
refutation_type=
"Refute: Add an Unobserved Common Cause")
results_matrix[i][
j] = refute.new_effect # Populate the results
refute.new_effect_array = results_matrix
refute.new_effect = (np.min(results_matrix),
np.max(results_matrix))
# Store the values into the refute object
refute.add_refuter(self)
if self.plotmethod is None:
return refute
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(6, 5))
left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
ax = fig.add_axes([left, bottom, width, height])
oe = self._estimate.value
contour_levels = [oe / 4.0, oe / 2.0, (3.0 / 4) * oe, oe]
contour_levels.extend(
[0, np.min(results_matrix),
np.max(results_matrix)])
if self.plotmethod == "contour":
cp = plt.contourf(self.kappa_y,
self.kappa_t,
results_matrix,
levels=sorted(contour_levels))
# Adding a label on the contour line for the original estimate
fmt = {}
trueeffect_index = np.where(cp.levels == oe)[0][0]
fmt[cp.levels[trueeffect_index]] = "Estimated Effect"
# Label every other level using strings
plt.clabel(cp, [cp.levels[trueeffect_index]],
inline=True,
fmt=fmt)
plt.colorbar(cp)
elif self.plotmethod == "colormesh":
cp = plt.pcolormesh(self.kappa_y,
self.kappa_t,
results_matrix,
shading="nearest")
plt.colorbar(cp, ticks=contour_levels)
ax.yaxis.set_ticks(self.kappa_t)
ax.xaxis.set_ticks(self.kappa_y)
plt.xticks(rotation=45)
ax.set_title('Effect of Unobserved Common Cause')
ax.set_ylabel('Value of Linear Constant on Treatment')
ax.set_xlabel('Value of Linear Constant on Outcome')
plt.show()
return refute
elif isinstance(self.kappa_t, (list, np.ndarray)):
outcomes = np.random.rand(len(self.kappa_t))
orig_data = copy.deepcopy(self._data)
for i in range(0, len(self.kappa_t)):
new_data = self.include_confounders_effect(
orig_data, self.kappa_t[i], self.kappa_y)
new_estimator = CausalEstimator.get_estimator_object(
new_data, self._target_estimand, self._estimate)
new_effect = new_estimator.estimate_effect()
refute = CausalRefutation(
self._estimate.value,
new_effect.value,
refutation_type="Refute: Add an Unobserved Common Cause"
)
self.logger.debug(refute)
outcomes[i] = refute.new_effect # Populate the results
refute.new_effect_array = outcomes
refute.new_effect = (np.min(outcomes), np.max(outcomes))
refute.add_refuter(self)
if self.plotmethod is None:
return refute
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(6, 5))
left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
ax = fig.add_axes([left, bottom, width, height])
plt.plot(self.kappa_t, outcomes)
plt.axhline(self._estimate.value, linestyle='--', color="gray")
ax.set_title('Effect of Unobserved Common Cause')
ax.set_xlabel('Value of Linear Constant on Treatment')
ax.set_ylabel('Estimated Effect after adding the common cause')
plt.show()
return refute
elif isinstance(self.kappa_y, (list, np.ndarray)):
outcomes = np.random.rand(len(self.kappa_y))
orig_data = copy.deepcopy(self._data)
for i in range(0, len(self.kappa_y)):
new_data = self.include_confounders_effect(
orig_data, self.kappa_t, self.kappa_y[i])
new_estimator = CausalEstimator.get_estimator_object(
new_data, self._target_estimand, self._estimate)
new_effect = new_estimator.estimate_effect()
refute = CausalRefutation(
self._estimate.value,
new_effect.value,
refutation_type="Refute: Add an Unobserved Common Cause"
)
self.logger.debug(refute)
outcomes[i] = refute.new_effect # Populate the results
refute.new_effect_array = outcomes
refute.new_effect = (np.min(outcomes), np.max(outcomes))
refute.add_refuter(self)
if self.plotmethod is None:
return refute
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(6, 5))
left, bottom, width, height = 0.1, 0.1, 0.8, 0.8
ax = fig.add_axes([left, bottom, width, height])
plt.plot(self.kappa_y, outcomes)
plt.axhline(self._estimate.value, linestyle='--', color="gray")
ax.set_title('Effect of Unobserved Common Cause')
ax.set_xlabel('Value of Linear Constant on Outcome')
ax.set_ylabel('Estimated Effect after adding the common cause')
plt.show()
return refute
