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_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_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 < 0.01)
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 - 1.0) < 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 - 1.0) < TOL
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 ) )
g.add_edges_from([('x1', 'x2'), ('x1', 'x3'), ('x2', 'x4'), ('x3', 'x4')])
adjustment = AdjustForDirectCauses()
admissable_set = adjustment.admissable_set(g, ['x2'], ['x3'])
print(admissable_set)
# generate some toy data:
SIZE = 2000
x1 = numpy.random.normal(size=SIZE)
x2 = x1 + numpy.random.normal(size=SIZE)
x3 = x1 + numpy.random.normal(size=SIZE)
x4 = x2 + x3 + numpy.random.normal(size=SIZE)
x5 = x4 + numpy.random.normal(size=SIZE)
# load the data into a dataframe:
X = pd.DataFrame({'x1': x1, 'x2': x2, 'x3': x3, 'x4': x4, 'x5': x5})
# define the variable types: 'c' is 'continuous'. The variables defined here
# are the ones the search is performed over -- NOT all the variables defined
# in the data frame.
variable_types = {'x1': 'c', 'x2': 'c', 'x3': 'c', 'x4': 'c', 'x5': 'c'}
effect = CausalEffect(X, ['x2'], ['x3'],
variable_types=variable_types,
admissable_set=list(admissable_set))
x = pd.DataFrame({'x2': [0.], 'x3': [0.]})
print(effect.pdf(x))
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)