Python CausalEffect.pdf示例

示例#1

文件： nonparametric.py 项目： akelleh/causality

 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 ) 

示例#2

文件： nonparametric.py 项目： LiuFang816/SALSTM_py_data

 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)

示例#3

文件： nonparametric.py 项目： n8sty/causality

 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

示例#4

文件： nonparametric.py 项目： n8sty/causality

 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

示例#5

文件： nonparametric.py 项目： akelleh/causality

 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 )

示例#6

文件： nonparametric.py 项目： LiuFang816/SALSTM_py_data

 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)

示例#7

文件： nonparametric_estimation.py 项目： j1nma/counterfactuals

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))

示例#8

文件： 因果分析.py 项目： SunshineHJian/--hujian

# in the data frame.
variable_types = {'x1': 'c', 'x2': 'c', 'x3': 'c', 'x4': 'c', 'x5': 'c'}
g = DiGraph()

g.add_nodes_from(['x1', 'x2', 'x3', 'x4', 'x5'])
g.add_edges_from([('x1', 'x2'), ('x1', 'x3'), ('x2', 'x4'), ('x3', 'x4')])
adjustment = AdjustForDirectCauses()
print(adjustment.admissable_set(g, ['x2'], ['x3']))
# set(['x1'])
from causality.estimation.nonparametric import CausalEffect
admissable_set = adjustment.admissable_set(g, ['x2'], ['x3'])
effect = CausalEffect(X, ['x2'], ['x3'],
                      variable_types=variable_types,
                      admissable_set=list(admissable_set))
x = pd.DataFrame({'x2': [0.], 'x3': [0.]})
res = effect.pdf(x)
print(res)

# 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