def test_expectation_discrete(self):
causes = ['c']
effects = ['d']
admissable_set = ['a']
variable_types={'a': 'u','b': 'u','c': 'u','d' : 'u'}
effect = CausalEffect(self.discrete,
causes,
effects,
admissable_set,
variable_types,
density=False,
expectation=True)
x = pd.DataFrame({ 'c' : [0]})
p = effect.expected_value(x)
print "p(d=1 | do(c = 0) ): ", p
assert( abs( 0.40 - p ) < 0.05 )
x = pd.DataFrame({ 'c' : [1]})
p = effect.expected_value(x)
print "p(d=1 | do(c = 1) ): ", p
assert( abs( 0.40 - p ) < 0.05 )
causes = ['b']
effects = ['d']
admissable_set = ['a']
variable_types={'a': 'u','b': 'u','c': 'u','d' : 'u'}
effect = CausalEffect(self.discrete,
causes,
effects,
admissable_set,
variable_types,
density=False,
expectation=True)
x = pd.DataFrame({ 'b' : [0]})
p = effect.expected_value(x)
print "p(d=1 | do(b = 0) ): ", p
assert( abs( p - 0.75 ) < 0.05 )
x = pd.DataFrame({ 'b' : [1]})
p = effect.expected_value(x)
print "p(d=1 | do(b = 1) ): ",p
assert( abs( p - 0.25 ) < 0.05 )
def test_expectation_continuous(self):
causes = ['c']
effects = ['d']
admissable_set = ['a']
variable_types={'a': 'c','b': 'c','c': 'c','d' : 'c'}
effect = CausalEffect(self.X,
causes,
effects,
admissable_set,
variable_types,
density=False,
expectation=True)
x = pd.DataFrame({ 'c' : [400]})
p1 = effect.expected_value(x)
print "E(d | do(c = 400) ): ", p1
x = pd.DataFrame({ 'c' : [600]})
p2 = effect.expected_value(x)
print "E(d | do(c = 600) ): ", p2
assert( abs( p2 - p1 ) / 200 < 0.5 )
causes = ['b']
effects = ['d']
admissable_set = ['a']
variable_types={'a': 'c','b': 'c','c': 'c','d' : 'c'}
effect = CausalEffect(self.X,
causes,
effects,
admissable_set,
variable_types,
density=False,
expectation=True)
x = pd.DataFrame({ 'b' : [400]})
p1 = effect.expected_value(x)
print "E(d | do(b = 400) ): ", p1
x = pd.DataFrame({ 'b' : [600]})
p2 = effect.expected_value(x)
print "E(d | do(b = 600) ): ",p2
#assert( abs( p - 0.25 ) < 0.05 )
assert( abs( ( p2 - p1 ) / 200 - 5. < 0.01 ) )
def test_expectation_discrete(self):
causes = ['c']
effects = ['d']
admissable_set = ['a']
variable_types = {'a': 'u', 'b': 'u', 'c': 'u', 'd': 'u'}
effect = CausalEffect(self.discrete,
causes,
effects,
admissable_set,
variable_types,
density=False,
expectation=True)
x = pd.DataFrame({'c': [0]})
p = effect.expected_value(x)
print "p(d=1 | do(c = 0) ): ", p
assert (abs(0.40 - p) < 0.05)
x = pd.DataFrame({'c': [1]})
p = effect.expected_value(x)
print "p(d=1 | do(c = 1) ): ", p
assert (abs(0.40 - p) < 0.05)
causes = ['b']
effects = ['d']
admissable_set = ['a']
variable_types = {'a': 'u', 'b': 'u', 'c': 'u', 'd': 'u'}
effect = CausalEffect(self.discrete,
causes,
effects,
admissable_set,
variable_types,
density=False,
expectation=True)
x = pd.DataFrame({'b': [0]})
p = effect.expected_value(x)
print "p(d=1 | do(b = 0) ): ", p
assert (abs(p - 0.75) < 0.05)
x = pd.DataFrame({'b': [1]})
p = effect.expected_value(x)
print "p(d=1 | do(b = 1) ): ", p
assert (abs(p - 0.25) < 0.05)
def test_expectation_continuous(self):
causes = ['c']
effects = ['d']
admissable_set = ['a']
variable_types = {'a': 'c', 'b': 'c', 'c': 'c', 'd': 'c'}
effect = CausalEffect(self.X,
causes,
effects,
admissable_set,
variable_types,
density=False,
expectation=True)
x = pd.DataFrame({'c': [400]})
p1 = effect.expected_value(x)
print "E(d | do(c = 400) ): ", p1
x = pd.DataFrame({'c': [600]})
p2 = effect.expected_value(x)
print "E(d | do(c = 600) ): ", p2
assert (abs(p2 - p1) / 200 < 0.5)
causes = ['b']
effects = ['d']
admissable_set = ['a']
variable_types = {'a': 'c', 'b': 'c', 'c': 'c', 'd': 'c'}
effect = CausalEffect(self.X,
causes,
effects,
admissable_set,
variable_types,
density=False,
expectation=True)
x = pd.DataFrame({'b': [400]})
p1 = effect.expected_value(x)
print "E(d | do(b = 400) ): ", p1
x = pd.DataFrame({'b': [600]})
p2 = effect.expected_value(x)
print "E(d | do(b = 600) ): ", p2
#assert( abs( p - 0.25 ) < 0.05 )
assert (abs((p2 - p1) / 200 - 5. < 0.01))
