def test_functional(self):
vertices = ["M", "A", "Y"]
di_edges = [("A", "M"), ("M", "Y")]
bi_edges = [("A", "Y")]
G = ADMG(vertices, di_edges, bi_edges)
one_id = OneLineID(G, ['A'], ['Y'])
functional = one_id.functional()
self.assertEqual("ΣM ΦAY(p(V);G) ΦAM(p(V);G) ", functional)
def test_two_var_id_graph(self):
vertices = ["A", "D", "C", "Y"]
di_edges = [('D', 'Y'), ('D', 'A'), ('A', 'Y'), ('C', 'A'), ('C', 'Y')]
bi_edges = []
G = ADMG(vertices, di_edges, bi_edges)
one_id = OneLineID(G, ['A'], ['Y', 'D'])
self.assertEqual({"C", "D", "Y"}, one_id.ystar)
self.assertTrue(one_id.id())
def test_BMAY_graph(self):
vertices = ["B", "M", "A", "Y"]
di_edges = [("B", "M"), ("M", "A"), ("A", "Y")]
bi_edges = [("B", "A"), ("B", "Y")]
G = ADMG(vertices, di_edges, bi_edges)
one_id = OneLineID(G, ["A"], ["Y"])
one_id.draw_swig()
self.assertTrue(one_id.id())
def test_non_id_graph(self):
"""
Test that Y(a,b) is not identified
"""
# non ID test
vertices = ['A', 'B', 'C', 'D', 'Y']
di_edges = [('A', 'B'), ('A', 'D'), ('B', 'C'), ('C', 'Y'), ('B', 'D'),
('D', 'Y')]
bi_edges = [('A', 'C'), ('B', 'Y'), ('B', 'D')]
G = ADMG(vertices, di_edges, bi_edges)
one_id = OneLineID(G, ['A', 'B'], ['Y'])
self.assertFalse(one_id.id())
def test_id_graph(self):
"""
Test that ADMG Y(a) is identified
"""
vertices = ['A', 'B', 'C', 'D', 'Y']
di_edges = [('A', 'B'), ('A', 'D'), ('B', 'C'), ('C', 'Y'), ('B', 'D'),
('D', 'Y')]
bi_edges = [('A', 'C'), ('B', 'Y'), ('B', 'D')]
G = ADMG(vertices, di_edges, bi_edges)
one_id = OneLineID(G, ['A'], ['Y'])
one_id.draw_swig()
self.assertTrue(one_id.id())
self.assertEqual({'Y', 'C', 'D', 'B'}, one_id.ystar)
one_id.export_intermediates()
def __init__(self, graph, treatment, outcome):
"""
Constructor.
:param graph: ADMG corresponding to substantive knowledge/model.
:param treatment: name of vertex corresponding to the treatment.
:param outcome: name of vertex corresponding to the outcome.
"""
self.graph = copy.deepcopy(graph)
self.treatment = treatment
self.outcome = outcome
self.strategy = None
self.is_mb_shielded = self.graph.mb_shielded()
# a dictionary of names for available estimators
self.estimators = {
"ipw": self._ipw,
"gformula": self._gformula,
"aipw": self._aipw,
"eff-aipw": self._eff_augmented_ipw,
"p-ipw": self._primal_ipw,
"d-ipw": self._dual_ipw,
"apipw": self._augmented_primal_ipw,
"eff-apipw": self._eff_augmented_primal_ipw,
"n-ipw": self._nested_ipw,
"anipw": self._augmented_nested_ipw
}
# a dictionary of names for available modeling strategies
self.models = {
"glm-binary": self._fit_binary_glm,
"glm-continuous": self._fit_continuous_glm
}
# check if the query is ID
self.one_id = OneLineID(graph, [treatment], [outcome])
# check the fixability criteria for the treatment
if len(
self.graph.district(treatment).intersection(
self.graph.descendants([treatment]))) == 1:
self.strategy = "a-fixable"
if self.is_mb_shielded:
print(
"\n Treatment is a-fixable and graph is mb-shielded. \n\n Available estimators are:\n \n"
+ "1. IPW (ipw)\n" + "2. Outcome regression (gformula)\n" +
"3. Generalized AIPW (aipw)\n" +
"4. Efficient Generalized AIPW (eff-aipw) \n \n" +
"Suggested estimator is Efficient Generalized AIPW \n")
else:
print(
"\n Treatment is a-fixable.\n\n Available estimators are :\n \n"
+ "1. IPW (ipw)\n" + "2. Outcome regression (gformula)\n" +
"3. Generalized AIPW (aipw)\n \n" +
"Suggested estimator is Generalized AIPW \n")
elif len(
self.graph.district(treatment).intersection(
self.graph.children([treatment]))) == 0:
self.strategy = "p-fixable"
if self.is_mb_shielded:
print(
"\n Treatment is p-fixable and graph is mb-shielded. \n\n Available estimators are:\n\n"
+ "1. Primal IPW (p-ipw)\n" + "2. Dual IPW (d-ipw)\n" +
"3. APIPW (apipw)\n" +
"4. Efficient APIPW (eff-apipw) \n \n" +
"Suggested estimator is Efficient APIPW \n")
else:
print(
"\n Treatment is p-fixable. \n\n Available estimators are:\n \n"
+ "1. Primal IPW (p-ipw)\n" + "2. Dual IPW (d-ipw)\n" +
"3. APIPW (apipw) \n\n" +
"Suggested estimator is APIPW \n")
elif self.one_id.id():
self.strategy = "nested-fixable"
print("\n Effect is identified. \n \n Available estimators:\n \n" +
"1. Nested IPW (n-ipw)\n" +
"2. Augmented NIPW (anipw) \n\n" +
"Suggested estimator is Augmented NIPW \n")
else:
self.strategy = "Not ID"
print("Effect is not identified! \n")
# finally, fix a valid topological order for estimation queries
self.p_order = self._find_valid_order(
"p-fixable") # used for a-fixable/p-fixable strategies
self.n_order = self._find_valid_order(
"nested-fixable") # used for nested-fixable strategies
self.state_space_map_ = {
} # maps from variable names to state space of the variable (binary/continuous)
class CausalEffect:
"""
Provides an interface to various estimation strategies for the ACE: E[Y(1) - Y(0)].
"""
def __init__(self, graph, treatment, outcome):
"""
Constructor.
:param graph: ADMG corresponding to substantive knowledge/model.
:param treatment: name of vertex corresponding to the treatment.
:param outcome: name of vertex corresponding to the outcome.
"""
self.graph = copy.deepcopy(graph)
self.treatment = treatment
self.outcome = outcome
self.strategy = None
self.is_mb_shielded = self.graph.mb_shielded()
# a dictionary of names for available estimators
self.estimators = {
"ipw": self._ipw,
"gformula": self._gformula,
"aipw": self._aipw,
"eff-aipw": self._eff_augmented_ipw,
"p-ipw": self._primal_ipw,
"d-ipw": self._dual_ipw,
"apipw": self._augmented_primal_ipw,
"eff-apipw": self._eff_augmented_primal_ipw,
"n-ipw": self._nested_ipw,
"anipw": self._augmented_nested_ipw
}
# a dictionary of names for available modeling strategies
self.models = {
"glm-binary": self._fit_binary_glm,
"glm-continuous": self._fit_continuous_glm
}
# check if the query is ID
self.one_id = OneLineID(graph, [treatment], [outcome])
# check the fixability criteria for the treatment
if len(
self.graph.district(treatment).intersection(
self.graph.descendants([treatment]))) == 1:
self.strategy = "a-fixable"
if self.is_mb_shielded:
print(
"\n Treatment is a-fixable and graph is mb-shielded. \n\n Available estimators are:\n \n"
+ "1. IPW (ipw)\n" + "2. Outcome regression (gformula)\n" +
"3. Generalized AIPW (aipw)\n" +
"4. Efficient Generalized AIPW (eff-aipw) \n \n" +
"Suggested estimator is Efficient Generalized AIPW \n")
else:
print(
"\n Treatment is a-fixable.\n\n Available estimators are :\n \n"
+ "1. IPW (ipw)\n" + "2. Outcome regression (gformula)\n" +
"3. Generalized AIPW (aipw)\n \n" +
"Suggested estimator is Generalized AIPW \n")
elif len(
self.graph.district(treatment).intersection(
self.graph.children([treatment]))) == 0:
self.strategy = "p-fixable"
if self.is_mb_shielded:
print(
"\n Treatment is p-fixable and graph is mb-shielded. \n\n Available estimators are:\n\n"
+ "1. Primal IPW (p-ipw)\n" + "2. Dual IPW (d-ipw)\n" +
"3. APIPW (apipw)\n" +
"4. Efficient APIPW (eff-apipw) \n \n" +
"Suggested estimator is Efficient APIPW \n")
else:
print(
"\n Treatment is p-fixable. \n\n Available estimators are:\n \n"
+ "1. Primal IPW (p-ipw)\n" + "2. Dual IPW (d-ipw)\n" +
"3. APIPW (apipw) \n\n" +
"Suggested estimator is APIPW \n")
elif self.one_id.id():
self.strategy = "nested-fixable"
print("\n Effect is identified. \n \n Available estimators:\n \n" +
"1. Nested IPW (n-ipw)\n" +
"2. Augmented NIPW (anipw) \n\n" +
"Suggested estimator is Augmented NIPW \n")
else:
self.strategy = "Not ID"
print("Effect is not identified! \n")
# finally, fix a valid topological order for estimation queries
self.p_order = self._find_valid_order(
"p-fixable") # used for a-fixable/p-fixable strategies
self.n_order = self._find_valid_order(
"nested-fixable") # used for nested-fixable strategies
self.state_space_map_ = {
} # maps from variable names to state space of the variable (binary/continuous)
def _find_valid_order(self, order_type):
"""
Utility function to find a valid order that satisfies the requirements in
Semiparametric Inference (Bhattacharya, Nabi, & Shpitser 2020) paper.
:param order_type: string specifying if the topological order is required for p-fixable or n-fixable problems.
:return: list corresponding to a valid topological order.
"""
# if we are a-fixing or p-fixing, just ensure that the
# treatment appears after all its non-descendants
if order_type != "nested-fixable":
focal_vertices = {self.treatment}
# otherwise we ensure the vertices in Y* that intersect with district
# of the treatment appear after all their non-descendants
else:
focal_vertices = self.one_id.ystar.intersection(
self.graph.district(self.treatment))
# perform a sort on the nondescendants first
nondescendants = set(
self.graph.vertices) - self.graph.descendants(focal_vertices)
order = self.graph.subgraph(nondescendants).topological_sort()
# then sort the focal vertices and its descendants
order += self.graph.subgraph(
self.graph.descendants(focal_vertices)).topological_sort()
return order
def _fit_binary_glm(self, data, formula, weights=None):
"""
Fit a binary GLM to data given a formula.
:param data: pandas data frame containing the data.
:param formula: string encoding an R-style formula e.g: Y ~ X1 + X2.
:return: the fitted model.
"""
return sm.GLM.from_formula(formula,
data=data,
family=sm.families.Binomial(),
freq_weights=weights).fit()
def _fit_continuous_glm(self, data, formula, weights=None):
"""
Fit a linear GLM to data given a formula.
:param data: pandas data frame containing the data.
:param formula: string encoding an R-style formula: e.g. Y ~ X1 + X2.
:return: the fitted model.
"""
return sm.GLM.from_formula(formula,
data=data,
family=sm.families.Gaussian(),
freq_weights=weights).fit()
def _ipw(self, data, assignment, model_binary=None, model_continuous=None):
"""
IPW estimator for the counterfactual mean E[Y(t)].
:param data: pandas data frame containing the data.
:param assignment: assignment value for treatment.
:param model_binary: string specifying modeling strategy to use for propensity score: e.g. glm-binary.
:param model_continuous: this argument is ignored for IPW.
:return: float corresponding to computed E[Y(t)].
"""
# pedantic checks to make sure the method returns valid estimates
if self.strategy != "a-fixable":
raise RuntimeError(
"IPW will not return valid estimates as treatment is not a-fixable"
)
# extract outcome from data frame and compute Markov pillow of treatment
Y = data[self.outcome]
mp_T = self.graph.markov_pillow([self.treatment], self.p_order)
if len(mp_T) != 0:
# fit T | mp(T) and compute probability of treatment for each sample
formula = self.treatment + " ~ " + '+'.join(mp_T) # + "+ ones"
model = model_binary(data, formula)
prob_T = model.predict(data)
else:
prob_T = np.ones(len(data)) * np.mean(data[self.treatment])
indices_T0 = data.index[data[self.treatment] == 0]
prob_T[indices_T0] = 1 - prob_T[indices_T0]
# compute IPW estimate
indices = data[self.treatment] == assignment
return np.mean((indices / prob_T) * Y)
def _gformula(self,
data,
assignment,
model_binary=None,
model_continuous=None):
"""
Outcome regression estimator for the counterfactual mean E[Y(t)].
:param data: pandas data frame containing the data.
:param assignment: assignment value for treatment.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for outcome regression: e.g. glm-continuous.
:return: float corresponding to computed E[Y(t)].
"""
# pedantic checks to make sure the method returns valid estimates
if self.strategy != "a-fixable":
raise RuntimeError(
"g-formula will not return valid estimates as treatment is not a-fixable"
)
# create a dataset where T=t
data_assign = data.copy()
data_assign[self.treatment] = assignment
# fit Y | T=t, mp(T)
mp_T = self.graph.markov_pillow([self.treatment], self.p_order)
if len(mp_T) != 0:
formula = self.outcome + " ~ " + self.treatment + '+' + '+'.join(
mp_T)
# predict outcome appropriately depending on binary vs continuous
else:
formula = self.outcome + " ~ " + self.treatment
if self.state_space_map_[self.outcome] == "binary":
model = model_binary(data, formula)
else:
model = model_continuous(data, formula)
return np.mean(model.predict(data_assign))
def _aipw(self,
data,
assignment,
model_binary=None,
model_continuous=None):
"""
Augmented IPW estimator for the counterfactual mean E[Y(t)].
:param data: pandas data frame containing the data.
:param assignment: assignment value for treatment.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for continuous variables: e.g. glm-continuous.
:return: float corresponding to computed E[Y(t)].
"""
# pedantic checks to make sure the method returns valid estimates
if self.strategy != "a-fixable":
raise RuntimeError(
"Augmented IPW will not return valid estimates as treatment is not a-fixable"
)
# extract the outcome and get Markov pillow of the treatment
Y = data[self.outcome]
mp_T = self.graph.markov_pillow([self.treatment], self.p_order)
if len(mp_T) != 0:
# fit T | mp(T) and predict treatment probabilities
formula_T = self.treatment + " ~ " + '+'.join(mp_T) #+ "+ ones"
model = model_binary(data, formula_T)
prob_T = model.predict(data)
formula_Y = self.outcome + " ~ " + self.treatment + '+' + '+'.join(
mp_T)
else:
prob_T = np.ones(len(data)) * np.mean(data[self.treatment])
formula_Y = self.outcome + " ~ " + self.treatment
indices_T0 = data.index[data[self.treatment] == 0]
prob_T[indices_T0] = 1 - prob_T[indices_T0]
indices = data[self.treatment] == assignment
# fit Y | T=t, mp(T) and predict outcomes under assignment T=t
data_assign = data.copy()
data_assign[self.treatment] = assignment
if self.state_space_map_[self.outcome] == "binary":
model = model_binary(data, formula_Y)
else:
model = model_continuous(data, formula_Y)
Yhat_vec = model.predict(data_assign)
# return AIPW estimate
return np.mean((indices / prob_T) * (Y - Yhat_vec) + Yhat_vec)
def _eff_augmented_ipw(self,
data,
assignment,
model_binary=None,
model_continuous=None):
"""
Efficient augmented IPW estimator for the counterfactual mean E[Y(t)].
:param data: pandas data frame containing the data.
:param assignment: assignment value for treatment.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for continuous variables: e.g. glm-continuous.
:return: float corresponding to computed E[Y(t)].
"""
# pedantic checks to make sure the method returns valid estimates
if self.strategy != "a-fixable":
raise RuntimeError(
"Augmented IPW will not return valid estimates as treatment is not a-fixable"
)
if not self.is_mb_shielded:
raise RuntimeError(
"EIF will not return valid estimates as graph is not mb-shielded"
)
# extract the outcome and get Markov pillow of treatment
Y = data[self.outcome]
mp_T = self.graph.markov_pillow([self.treatment], self.p_order)
# fit T | mp(T) and compute treatment probabilities
if len(mp_T) != 0:
formula = self.treatment + " ~ " + '+'.join(mp_T) #+ "+ ones"
model = model_binary(data, formula)
prob_T = model.predict(data)
else:
prob_T = np.ones(len(data)) * np.mean(data[self.treatment])
indices_T0 = data.index[data[self.treatment] == 0]
prob_T[indices_T0] = 1 - prob_T[indices_T0]
indices = data[self.treatment] == assignment
# compute the primal estimates that we use to compute projections
primal = (indices / prob_T) * Y
data["primal"] = primal
eif_vec = 0
# get the variables that are actually involved in the efficient influence function
# TODO: prune extra variables
eif_vars = [V for V in self.graph.vertices]
eif_vars.remove(self.treatment)
# iterate over variables
for V in eif_vars:
# get the Markov pillow of the variable
mpV = self.graph.markov_pillow([V], self.p_order)
# compute E[primal | mp(V)]
formula = "primal ~ " + '+'.join(mpV)
# special case if mp(V) is empty, then E[primal | mp(V)] = E[primal]
if len(mpV) != 0:
model_mpV = model_continuous(
data, formula) # primal is a continuous r.v.
primal_mpV = model_mpV.predict(data)
else:
primal_mpV = np.mean(primal)
# compute E[primal | V, mp(V)]
formula = formula + "+" + V
model_VmpV = model_continuous(data, formula)
primal_VmpV = model_VmpV.predict(data)
# add contribution of current variable: E[primal | V, mp(V)] - E[primal | mp(V)]
eif_vec += primal_VmpV - primal_mpV
# re-add the primal so final result is not mean zero
eif_vec = eif_vec + np.mean(primal)
# return efficient AIPW estimate
return np.mean(eif_vec)
def _beta_primal(self,
data,
assignment,
model_binary=None,
model_continuous=None):
"""
Utility function to compute primal estimates for a dataset.
:param data: pandas data frame containing the data.
:param assignment: assignment value for treatment.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for continuous variables: e.g. glm-continuous.
:return: numpy array of floats corresponding to primal estimates for each sample.
"""
# extract the outcome
Y = data[self.outcome]
# C := pre-treatment vars and L := post-treatment vars in district of treatment
C = self.graph.pre([self.treatment], self.p_order)
post = set(self.graph.vertices).difference(C)
L = post.intersection(self.graph.district(self.treatment))
# create copies of the data with treatment assignments T=0 and T=1
data_T1 = data.copy()
data_T1[self.treatment] = 1
data_T0 = data.copy()
data_T0[self.treatment] = 0
indices = data[self.treatment] == assignment
# prob: stores \prod_{Li in L} p(Li | mp(Li))
# prob_T1: stores \prod_{Li in L} p(Li | mp(Li)) at T=1
# prob_T0: stores \prod_{Li in L} p(Li | mp(Li)) at T=0
mp_T = self.graph.markov_pillow([self.treatment], self.p_order)
indices_T0 = data.index[data[self.treatment] == 0]
if len(mp_T) != 0:
formula = self.treatment + " ~ " + '+'.join(mp_T)
model = model_binary(data, formula)
prob = model.predict(data)
prob[indices_T0] = 1 - prob[indices_T0]
prob_T1 = model.predict(data)
prob_T0 = 1 - prob_T1
else:
prob = np.ones(len(data)) * np.mean(data[self.treatment])
prob[indices_T0] = 1 - prob[indices_T0]
prob_T1 = np.ones(len(data)) * np.mean(data[self.treatment])
prob_T0 = 1 - prob_T1
# iterate over vertices in L (except the outcome)
for V in L.difference([self.treatment, self.outcome]):
# fit V | mp(V)
mp_V = self.graph.markov_pillow([V], self.p_order)
formula = V + " ~ " + '+'.join(mp_V)
# p(V =v | .), p(V = v | . , T=1), p(V = v | ., T=0)
if self.state_space_map_[V] == "binary":
model = model_binary(data, formula)
prob_V = model.predict(data)
prob_V_T1 = model.predict(data_T1)
prob_V_T0 = model.predict(data_T0)
indices_V0 = data.index[data[V] == 0]
# p(V | .), p(V | ., T=t)
prob_V[indices_V0] = 1 - prob_V[indices_V0]
prob_V_T1[indices_V0] = 1 - prob_V_T1[indices_V0]
prob_V_T0[indices_V0] = 1 - prob_V_T0[indices_V0]
else:
model = model_continuous(data, formula)
E_V = model.predict(data)
E_V_T1 = model.predict(data_T1)
E_V_T0 = model.predict(data_T0)
std = np.std(data[V] - E_V)
prob_V = norm.pdf(data[V], loc=E_V, scale=std)
prob_V_T1 = norm.pdf(data[V], loc=E_V_T1, scale=std)
prob_V_T0 = norm.pdf(data[V], loc=E_V_T0, scale=std)
prob *= prob_V
prob_T1 *= prob_V_T1
prob_T0 *= prob_V_T0
# special case when the outcome is in L
if self.outcome in L:
# fit a binary/continuous model for Y | mp(Y)
mp_Y = self.graph.markov_pillow([self.outcome], self.p_order)
formula = self.outcome + " ~ " + '+'.join(mp_Y)
if self.state_space_map_[self.outcome] == "binary":
model = model_binary(data, formula)
else:
model = model_continuous(data, formula)
# predict the outcome and adjust numerator of primal accordingly
Yhat_T1 = model.predict(data_T1)
Yhat_T0 = model.predict(data_T0)
prob_sumT = prob_T1 * Yhat_T1 + prob_T0 * Yhat_T0
beta_primal = indices * (prob_sumT / prob)
else:
prob_sumT = prob_T1 + prob_T0
beta_primal = indices * (prob_sumT / prob) * Y
return beta_primal
def _primal_ipw(self,
data,
assignment,
model_binary=None,
model_continuous=None):
"""
Primal IPW estimator for the counterfactual mean E[Y(t)].
:param data: pandas data frame containing the data.
:param assignment: assignment value for treatment.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for continuous variables: e.g. glm-continuous.
:return: float corresponding to computed E[Y(t)].
"""
# pedantic checks to make sure the method returns valid estimates
if self.strategy != "p-fixable" and self.strategy != "a-fixable":
raise RuntimeError(
"Primal IPW will not return valid estimates as treatment is not p-fixable"
)
# return primal IPW estimate
return np.mean(
self._beta_primal(data, assignment, model_binary,
model_continuous))
def _beta_dual(self,
data,
assignment,
model_binary=None,
model_continuous=None):
"""
Utility function to compute dual estimates for a dataset.
:param data: pandas data frame containing the data.
:param assignment: assignment value for treatment.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for continuous variables: e.g. glm-continuous.
:return: numpy array of floats corresponding to dual estimates for each sample.
"""
# extract the outcome
Y = data[self.outcome]
# M := inverse Markov pillow of the treatment
M = set([
m for m in self.graph.vertices
if self.treatment in self.graph.markov_pillow([m], self.p_order)
])
M = M.difference(self.graph.district(self.treatment))
# create a dataset where T=t
data_assigned = data.copy()
data_assigned[self.treatment] = assignment
prob = 1 # stores \prod_{Mi in M} p(Mi | mp(Mi))|T=t / p(Mi | mp(Mi))
for V in M.difference([self.outcome]):
# Fit V | mp(V)
mp_V = self.graph.markov_pillow([V], self.p_order)
formula = V + " ~ " + '+'.join(mp_V)
# p(V = 1 | .), p(V = 1 | . , T=assigned)
if self.state_space_map_[V] == "binary":
model = model_binary(data, formula)
prob_V = model.predict(data)
prob_V_assigned = model.predict(data_assigned)
indices_V0 = data.index[data[V] == 0]
# p(V | .) and p(V | ., T=assignment)
prob_V[indices_V0] = 1 - prob_V[indices_V0]
prob_V_assigned[indices_V0] = 1 - prob_V_assigned[indices_V0]
else:
model = model_continuous(data, formula)
E_V = model.predict(data)
E_V_assigned = model.predict(data_assigned)
std = np.std(data[V] - E_V)
prob_V = norm.pdf(data[V], loc=E_V, scale=std)
prob_V_assigned = norm.pdf(data[V],
loc=E_V_assigned,
scale=std)
prob *= prob_V_assigned / prob_V
# special case for if the outcome is in M
if self.outcome in M:
mp_Y = self.graph.markov_pillow([self.outcome], self.p_order)
formula = self.outcome + " ~ " + '+'.join(mp_Y)
if self.state_space_map_[self.outcome] == "binary":
model = model_binary(data, formula)
else:
model = model_continuous(data, formula)
Yhat_assigned = model.predict(data_assigned)
else:
Yhat_assigned = Y
return prob * Yhat_assigned
def _dual_ipw(self,
data,
assignment,
model_binary=None,
model_continuous=None):
"""
Dual IPW estimator for the counterfactual mean E[Y(t)].
:param data: pandas data frame containing the data.
:param assignment: assignment value for treatment.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for continuous variables: e.g. glm-continuous.
:return: float corresponding to computed E[Y(t)].
"""
# pedantic checks to make sure the method returns valid estimates
if self.strategy != "p-fixable" and self.strategy != "a-fixable":
raise RuntimeError(
"Dual IPW will not return valid estimates as treatment is not p-fixable"
)
# return primal dual IPW estimate
return np.mean(
self._beta_dual(data, assignment, model_binary, model_continuous))
def _augmented_primal_ipw(self,
data,
assignment,
model_binary=None,
model_continuous=None):
"""
Augmented primal IPW estimator for the counterfactual mean E[Y(t)].
:param data: pandas data frame containing the data.
:param assignment: assignment value for treatment.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for continuous variables: e.g. glm-continuous.
:return: float corresponding to computed E[Y(t)].
"""
# pedantic checks to make sure the method returns valid estimates
if self.strategy != "p-fixable" and self.strategy != "a-fixable":
raise RuntimeError(
"Augmented primal IPW will not return valid estimates as treatment is not p-fixable"
)
# compute the primal and dual estimates and them to the data frame
beta_primal = self._beta_primal(data, assignment, model_binary,
model_continuous)
beta_dual = self._beta_dual(data, assignment, model_binary,
model_continuous)
data["beta_primal"] = beta_primal
data["beta_dual"] = beta_dual
# C := pre-treatment vars
# L := post-treatment vars in the district of T
# M := post treatment vars not in L (a.k.a. the rest)
C = self.graph.pre([self.treatment], self.p_order)
post = set(self.graph.vertices).difference(C)
L = post.intersection(self.graph.district(self.treatment))
M = post - L
IF = 0
# iterate over all post-treatment variables
for V in post:
# compute all predecessors according to the topological order
pre_V = self.graph.pre([V], self.p_order)
# if the variables is in M, project using the primal otherwise use the dual
# to fit E[beta | pre(V)]
if V in M:
formula = "beta_primal" + " ~ " + '+'.join(pre_V)
elif V in L:
formula = "beta_dual" + " ~ " + '+'.join(pre_V)
# special logic for if there are no predecessors for the variable (which could only happen to T)
if len(pre_V) != 0:
model_preV = model_continuous(
data, formula) # primal/dual is a continuous r.v.
pred_preV = model_preV.predict(data)
else:
pred_preV = 0
# fit E[beta | V, pre(V)]
formula = formula + " + " + V
model_VpreV = model_continuous(data, formula)
pred_VpreV = model_VpreV.predict(data)
# add contribution of current variable as E[beta | V, pre(V)] - E[beta | pre(V)]
IF += pred_VpreV - pred_preV
# final contribution from E[beta | C] (if C is not empty)
if len(C) != 0:
formula = "beta_dual" + " ~ " + '+'.join(C)
model_C = model_continuous(data,
formula) # dual is a continuous r.v.
IF += model_C.predict(data)
# return APIPW estimate
return np.mean(IF)
def _eff_augmented_primal_ipw(self,
data,
assignment,
model_binary=None,
model_continuous=None):
"""
Efficient augmented primal IPW estimator for the counterfactual mean E[Y(t)].
:param data: pandas data frame containing the data.
:param assignment: assignment value for treatment.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for continuous variables: e.g. glm-continuous.
:return: float corresponding to computed E[Y(t)].
"""
# pedantic checks to make sure the method returns valid estimates
if self.strategy != "p-fixable" and self.strategy != "a-fixable":
raise RuntimeError(
"Augmented primal IPW will not return valid estimates as treatment is not p-fixable"
)
if not self.is_mb_shielded:
raise RuntimeError(
"EIF will not return valid estimates as graph is not mb-shielded"
)
# compute primal and dual estimates and add them to the data frame
beta_primal = self._beta_primal(data, assignment, model_binary,
model_continuous)
beta_dual = self._beta_dual(data, assignment, model_binary,
model_continuous)
data["beta_primal"] = beta_primal
data["beta_dual"] = beta_dual
# C := pre-treatment vars
# L := post-treatment vars in the district of T
# M := post treatment vars not in L (a.k.a. the rest)
C = self.graph.pre([self.treatment], self.p_order)
post = set(self.graph.vertices).difference(C)
L = post.intersection(self.graph.district(self.treatment))
M = post - L
IF = 0
# iterate over all variables
for V in self.graph.vertices:
# get the Markov pillow
mp_V = self.graph.markov_pillow([V], self.p_order)
# if the variables is in M, project using the primal otherwise use the dual
# to fit E[beta | mp(V)]
if V in M:
formula = "beta_primal" + " ~ " + '+'.join(mp_V)
else:
formula = "beta_dual" + " ~ " + '+'.join(mp_V)
# special logic for if there the Markov pillow is empty
if len(mp_V) != 0:
model_mpV = model_continuous(data, formula)
pred_mpV = model_mpV.predict(data)
else:
pred_mpV = np.mean(beta_dual)
# fit E[beta | V, mp(V)]
formula = formula + " + " + V
model_VmpV = model_continuous(data, formula)
pred_VmpV = model_VmpV.predict(data)
# add contribution of current variable as E[beta | V, mp(V)] - E[beta | mp(V)]
IF += pred_VmpV - pred_mpV
# add final contribution so that estimator is not mean-zero
IF += np.mean(beta_dual)
# return efficient APIPW estimate
return np.mean(IF)
def _fit_intrinsic_kernel(self,
data,
district,
model_binary=None,
model_continuous=None):
"""
Get estimates of an intrinsic kernel q_D(D|pa(D)) for each sample.
:param data: pandas data frame containing the data.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for continuous variables: e.g. glm-continuous.
:return: numpy array of estimated probabilities of the kernel for each sample.
"""
fixing_prob = np.ones(len(data))
# get parents of the district
parents = self.graph.parents(district) - district
# initialize with ancestral margin as fixing descendants are just sums
G = self.graph.subgraph(self.graph.ancestors(district))
# we know D is intrinsic because of prior checks
remaining_vertices = set(
G.vertices
) - district - parents # is subtracting parents here ok? think so..
while not G.fixable(parents)[0]:
fixed = False
# at every step try to simplify as much as possible by fixing
# childless vertices as these correspond to just sums
# also find a backup fixable vertex in case there are no childless ones
iter_gen = iter(remaining_vertices)
i = 0
while not fixed and i < len(remaining_vertices):
i += 1
V = next(iter_gen)
if len(G.children([V])) == 0:
G.fix([V])
fixed = True
remaining_vertices.remove(V)
elif G.fixable([V])[0]:
fixable_V = V
# if we fixed something go back and see if we can fix another childless vertex
# or if the requirement that parents are fixable is satisfied
if fixed:
continue
# otherwise do a true fix corresponding to reweighting
# using the fixable V that we found
V = fixable_V
mb_V = G.markov_blanket([V])
formula = V + " ~ " + '+'.join(mb_V)
# p(V = 1 | .)
if self.state_space_map_[V] == "binary":
model = model_binary(data, formula, 1 / fixing_prob)
prob_V = model.predict(data)
indices_V0 = data.index[data[V] == 0]
# p(V | .)
prob_V[indices_V0] = 1 - prob_V[indices_V0]
# handling continuous data
else:
model = model_continuous(data, formula, 1 / fixing_prob)
E_V = model.predict(data)
std = np.std(data[V] - E_V)
prob_V = norm.pdf(data[V], loc=E_V, scale=std)
fixing_prob *= prob_V
G.fix([V])
remaining_vertices.remove(V)
# get the Markov blanket of the parents in this final graph
mb_parents = G.markov_blanket(parents)
# iterate over each parent and get weights required to fix them
for V in parents:
# p(V = 1 | .)
mp_V = mb_parents.intersection(self.graph.pre([V], self.n_order))
if len(mp_V) != 0:
formula = V + " ~ " + '+'.join(mp_V)
else:
formula = V + " ~ -1 + 1"
if self.state_space_map_[V] == "binary":
model = model_binary(data, formula, 1 / fixing_prob)
prob_V = model.predict(data)
indices_V0 = data.index[data[V] == 0]
# p(V | .)
prob_V[indices_V0] = 1 - prob_V[indices_V0]
# handling continuous data
else:
model = model_continuous(data, formula, 1 / fixing_prob)
E_V = model.predict(data)
std = np.std(data[V] - E_V)
prob_V = norm.pdf(data[V], loc=E_V, scale=std)
fixing_prob *= prob_V
kernel_prob = np.ones(len(data))
# iterate over member of the intrinsic set and get the final kernel weights
for V in district:
# p(V = 1 | .)
mp_V = parents.union(
district.intersection(self.graph.pre(V, self.n_order)))
if len(mp_V) != 0:
formula = V + " ~ " + '+'.join(mp_V)
else:
formula = V + " ~ -1 + 1"
if self.state_space_map_[V] == "binary":
model = model_binary(data, formula, 1 / fixing_prob)
prob_V = model.predict(data)
indices_V0 = data.index[data[V] == 0]
# p(V | .)
prob_V[indices_V0] = 1 - prob_V[indices_V0]
# handling continuous data
else:
model = model_continuous(data, formula, 1 / fixing_prob)
E_V = model.predict(data)
std = np.std(data[V] - E_V)
prob_V = norm.pdf(data[V], loc=E_V, scale=std)
kernel_prob *= prob_V
return kernel_prob
def _get_nested_rebalanced_weights(self,
data,
model_binary=None,
model_continuous=None):
"""
Get the rebalancing weights required for nested IPW and augmented nested IPW
:param data: pandas data frame containing the data.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for continuous variables: e.g. glm-continuous.
:return: numpy array corresponding to rebalancing weights.
"""
# first get all districts of GY* that intersect with district of T
district_T = self.graph.district(self.treatment)
modified_districts = [
district for district in self.one_id.Gystar.districts
if len(district.intersection(district_T)) != 0
]
# placeholder for the rebalancing probability
rebalance_prob = np.ones(len(data))
# iterate over each modified district
for district in modified_districts:
# first compute weights in the denominator of \prod_{Vi in district} p(Vi | mp(Vi))
for V in district:
# Fit V | mp(V)
mp_V = self.graph.markov_pillow([V], self.n_order)
formula = V + " ~ " + '+'.join(mp_V)
# p(V = 1 | .)
if self.state_space_map_[V] == "binary":
model = model_binary(data, formula)
prob_V = model.predict(data)
indices_V0 = data.index[data[V] == 0]
# p(V | .)
prob_V[indices_V0] = 1 - prob_V[indices_V0]
# handling continuous data
else:
model = model_continuous(data, formula)
E_V = model.predict(data)
std = np.std(data[V] - E_V)
prob_V = norm.pdf(data[V], loc=E_V, scale=std)
rebalance_prob *= 1 / prob_V
# now compute the q_D(D | pa(D))
rebalance_prob *= self._fit_intrinsic_kernel(
data, district, model_binary, model_continuous)
return 1 / rebalance_prob
def _nested_ipw(self,
data,
assignment,
model_binary=None,
model_continuous=None):
"""
Nested IPW estimator for the counterfactual mean E[Y(t)].
:param data: pandas data frame containing the data.
:param assignment: assignment value for treatment.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for continuous variables: e.g. glm-continuous.
:return: float corresponding to computed E[Y(t)].
"""
# pedantic checks to make sure the method returns valid estimates
if self.strategy == "Not ID":
raise RuntimeError(
"Nested IPW will not return valid estimates as causal effect is not identified"
)
# fit T | mp(T) with the rebalanced weights and compute the nested IPW
rebalance_weights = self._get_nested_rebalanced_weights(
data, model_binary, model_continuous)
# extract outcome from data frame and compute Markov pillow of treatment
Y = data[self.outcome]
mp_T = self.graph.markov_pillow([self.treatment], self.n_order)
if len(mp_T) != 0:
# fit T | mp(T) and compute probability of treatment for each sample
formula = self.treatment + " ~ " + '+'.join(mp_T)
model = model_binary(data, formula, weights=rebalance_weights)
prob_T = model.predict(data)
else:
prob_T = np.ones(len(data)) * np.average(data[self.treatment],
weights=rebalance_weights)
indices_T0 = data.index[data[self.treatment] == 0]
prob_T[indices_T0] = 1 - prob_T[indices_T0]
# compute nested IPW estimate
indices = data[self.treatment] == assignment
return np.mean((indices / prob_T) * Y)
def _augmented_nested_ipw(self,
data,
assignment,
model_binary=None,
model_continuous=None):
"""
Augmented nested IPW estimator for the counterfactual mean E[Y(t)].
:param data: pandas data frame containing the data.
:param assignment: assignment value for treatment.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for continuous variables: e.g. glm-continuous.
:return: float corresponding to computed E[Y(t)].
"""
if self.strategy == "Not ID":
raise RuntimeError(
"Nested IPW will not return valid estimates as causal effect is not identified"
)
# get the rebalancing weights
rebalance_weights = self._get_nested_rebalanced_weights(
data, model_binary, model_continuous)
# extract the outcome and get Markov pillow of the treatment
Y = data[self.outcome]
mp_T = self.graph.markov_pillow([self.treatment], self.n_order)
if len(mp_T) != 0:
# fit T | mp(T) and predict treatment probabilities
formula_T = self.treatment + " ~ " + '+'.join(mp_T) # + "+ ones"
model = model_binary(data, formula_T, weights=rebalance_weights)
prob_T = model.predict(data)
formula_Y = self.outcome + " ~ " + self.treatment + '+' + '+'.join(
mp_T)
else:
prob_T = np.ones(len(data)) * np.average(data[self.treatment],
weights=rebalance_weights)
formula_Y = self.outcome + " ~ " + self.treatment
indices_T0 = data.index[data[self.treatment] == 0]
prob_T[indices_T0] = 1 - prob_T[indices_T0]
indices = data[self.treatment] == assignment
# fit Y | T=t, mp(T) and predict outcomes under assignment T=t
data_assign = data.copy()
data_assign[self.treatment] = assignment
if self.state_space_map_[self.outcome] == "binary":
model = model_binary(data, formula_Y, weights=rebalance_weights)
else:
model = model_continuous(data,
formula_Y,
weights=rebalance_weights)
Yhat_vec = model.predict(data_assign)
# return ANIPW estimate
return np.mean((indices / prob_T) * (Y - Yhat_vec) + Yhat_vec)
def compute_effect(self,
data,
estimator,
model_binary=None,
model_continuous=None,
n_bootstraps=0,
alpha=0.05):
"""
Bootstrap functionality to compute the Average Causal Effect if the outcome is continuous
or the Causal Odds Ratio if the outcome is binary. Returns the point estimate
as well as lower and upper quantiles for a user specified confidence level.
:param data: pandas data frame containing the data.
:param estimator: string indicating what estimator to use: e.g. eff-apipw.
:param model_binary: string specifying modeling strategy to use for binary variables: e.g. glm-binary.
:param model_continuous: string specifying modeling strategy to use for continuous variables: e.g. glm-continuous.
:param n_bootstraps: number of bootstraps.
:param alpha: the significance level with the default value of 0.05.
:return: one float corresponding to ACE/OR if n_bootstraps=0, else three floats corresponding to ACE/OR, lower quantile, upper quantile.
"""
# instantiate modeling strategy with defaults
if not model_binary:
model_binary = self._fit_binary_glm
if not model_continuous:
model_continuous = self._fit_continuous_glm
# add a column of ones to fit intercept terms
data['ones'] = np.ones(len(data))
# get state space of all variables
for colname, colvalues in data.iteritems():
if set([0, 1]).issuperset(colvalues.unique()):
self.state_space_map_[colname] = "binary"
else:
self.state_space_map_[colname] = "continuous"
# instantiate estimator and get point estimate of ACE
method = self.estimators[estimator]
point_estimate_T1 = method(data, 1, model_binary, model_continuous)
point_estimate_T0 = method(data, 0, model_binary, model_continuous)
# if Y is binary report log of odds ration, if Y is continuous report ACE
if self.state_space_map_[self.outcome] == "binary":
ace = np.log((point_estimate_T1 / (1 - point_estimate_T1)) /
(point_estimate_T0 / (1 - point_estimate_T0)))
else:
ace = point_estimate_T1 - point_estimate_T0
if n_bootstraps > 0:
ace_vec = []
Ql = alpha / 2
Qu = 1 - alpha / 2
# iterate over bootstraps
for iter in range(n_bootstraps):
# resample the data with replacement
data_sampled = data.sample(len(data), replace=True)
data_sampled.reset_index(drop=True, inplace=True)
# estimate ACE in resampled data
estimate_T1 = method(data_sampled, 1, model_binary,
model_continuous)
estimate_T0 = method(data_sampled, 0, model_binary,
model_continuous)
# if Y is binary report log of odds ration, if Y is continuous report ACE
if self.state_space_map_[self.outcome] == "binary":
ace_vec.append(
np.log((estimate_T1 / (1 - estimate_T1)) /
(estimate_T0 / (1 - estimate_T0))))
else:
ace_vec.append(estimate_T1 - estimate_T0)
# calculate the quantiles
quantiles = np.quantile(ace_vec, q=[Ql, Qu])
q_low = quantiles[0]
q_up = quantiles[1]
return ace, q_low, q_up
return ace