def test_pdf_no_adjustment(self):
causes = ['c']
effects = ['d']
admissable_set = []
variable_types={'a': 'u','b': 'u','c': 'u','d' : 'u'}
effect = CausalEffect(self.discrete,causes,effects,admissable_set,variable_types)
# p(d=1|do(c=0) = 0.45, p(d=1|b=0) = 0.40
p = effect.pdf(pd.DataFrame({ 'd' : [1], 'c' : [0]}))
print p
assert( abs( 0.40 - p ) < 0.02 )
def test_pdf_no_adjustment(self):
causes = ['c']
effects = ['d']
admissable_set = []
variable_types = {'a': 'u', 'b': 'u', 'c': 'u', 'd': 'u'}
effect = CausalEffect(self.discrete, causes, effects, admissable_set,
variable_types)
# p(d=1|do(c=0) = 0.45, p(d=1|b=0) = 0.40
p = effect.pdf(pd.DataFrame({'d': [1], 'c': [0]}))
print p
assert (abs(0.40 - p) < 0.02)
def test_pdf_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)
c = np.mean(effect.support['c'])
d = np.mean(effect.support['d'])
e1 = effect.pdf(pd.DataFrame({ 'd' : [d], 'c' : [ 0.9 * c]}))
e2 = effect.pdf(pd.DataFrame({ 'd' : [d], 'c' : [ 1.1 * c]}))
print e2, e1, e2 - e1, (e2 - e1) / e2
assert( abs(e2 - e1) / e2 < 0.05 )
def test_pdf_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)
c = np.mean(effect.support['c'])
d = np.mean(effect.support['d'])
e1 = effect.pdf(pd.DataFrame({'d': [d], 'c': [0.9 * c]}))
e2 = effect.pdf(pd.DataFrame({'d': [d], 'c': [1.1 * c]}))
print e2, e1, e2 - e1, (e2 - e1) / e2
assert (abs(e2 - e1) / e2 < 0.05)
def test_integration_function(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)
def test_get_support(self):
data_ranges = {}
for variable in self.X.columns:
data_ranges[variable] = (self.X[variable].min(),
self.X[variable].max())
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)
for variable, (supp_min, supp_max) in effect.support.items():
(xmin, xmax) = data_ranges[variable]
assert (supp_min <= xmin)
assert (supp_max >= xmax)
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_densities(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 = lambda x: effect.density.pdf(data_predict=[x])
integral = nquad(density,
[effect.support[d_var]
for d_var in admissable_set])[0]
print integral
assert (abs(integral - 1.) < TOL)
x_vals = [np.mean(effect.support[var]) for var in causes]
z_vals = [np.mean(effect.support[var]) for var in admissable_set]
density = lambda x: effect.conditional_density.pdf(
endog_predict=[x], exog_predict=x_vals + z_vals)
integral = nquad(density,
[effect.support[d_var] for d_var in effects])[0]
print x_vals, z_vals, integral
assert (abs(integral - 1.) < TOL)
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 ) )