def test_score(self):
N = 100000
z1 = np.random.normal(size=N)
z2 = np.random.choice([0, 1], size=N)
z3 = np.random.choice(['a', 'b', 'c'], size=N)
numeric_mapping = {'a': 3, 'b': 4, 'c': 5}
z3_numeric = [numeric_mapping[z3i] for z3i in z3]
p_assign = np.exp(z1 + z2 +
z3_numeric) / (1. + np.exp(z1 + z2 + z3_numeric))
assignment = np.random.binomial(1, p_assign)
outcome = np.random.normal(assignment)
matcher = PropensityScoreMatching()
X = pd.DataFrame({
'z1': z1,
'z2': z2,
'z3': z3,
'assignment': assignment,
'outcome': outcome
})
confounder_types = {'z1': 'c', 'z2': 'o', 'z3': 'o'}
matcher.score(X, confounder_types, store_model_fit=True)
assert 0.9 <= matcher.model_fit.params['z1'] <= 1.1
assert 0.9 <= matcher.model_fit.params['z2'] <= 1.1
assert 0.0 <= matcher.model_fit.params['z3_b'] <= 2.0
assert 1.0 <= matcher.model_fit.params['z3_c'] <= 3.0
assert 2.0 <= matcher.model_fit.params['intercept'] <= 4.0
def test_score(self):
N = 5000
z1 = np.random.normal(size=N)
z2 = np.random.choice(['a','b','c'], size=N)
numeric_mapping = {'a' :3, 'b' :4, 'c' :5}
z2_numeric = [numeric_mapping[z2i] for z2i in z2]
p_assign = np.exp(z1 + z2_numeric) / (1. + np.exp(z1 + z2_numeric))
assignment = np.random.binomial(1, p_assign)
outcome = np.random.normal(assignment)
matcher = PropensityScoreMatching()
X = pd.DataFrame({'z1': z1, 'z2': z2, 'assignment': assignment, 'outcome': outcome})
confounder_types = {'z1': 'c', 'z2':'o'}
matcher.score(X, confounder_types, store_model_fit=True)
assert 0.7 <= matcher.propensity_score_model.params['z1'] <= 1.3
assert 0.0 <= matcher.propensity_score_model.params['z2_b'] <= 2.0
assert 1.0 <= matcher.propensity_score_model.params['z2_c'] <= 3.0
assert 2.0 <= matcher.propensity_score_model.params['intercept'] <= 4.0
def test_score(self):
N = 100000
z1 = np.random.normal(size=N)
z2 = np.random.choice([0, 1], size=N)
z3 = np.random.choice(["a", "b", "c"], size=N)
numeric_mapping = {"a": 3, "b": 4, "c": 5}
z3_numeric = [numeric_mapping[z3i] for z3i in z3]
p_assign = np.exp(z1 + z2 + z3_numeric) / (1.0 + np.exp(z1 + z2 + z3_numeric))
assignment = np.random.binomial(1, p_assign)
outcome = np.random.normal(assignment)
matcher = PropensityScoreMatching()
X = pd.DataFrame({"z1": z1, "z2": z2, "z3": z3, "assignment": assignment, "outcome": outcome})
confounder_types = {"z1": "c", "z2": "o", "z3": "o"}
matcher.score(X, confounder_types, store_model_fit=True)
assert 0.9 <= matcher.model_fit.params["z1"] <= 1.1
assert 0.9 <= matcher.model_fit.params["z2"] <= 1.1
assert 0.0 <= matcher.model_fit.params["z3_b"] <= 2.0
assert 1.0 <= matcher.model_fit.params["z3_c"] <= 3.0
assert 2.0 <= matcher.model_fit.params["intercept"] <= 4.0
def spx_wmOnBoard_tobin():
'''
Test the effect of having women on the board of directors on the tobins Q score
M&M:
For the American companies inside the S&P 500 index,
we found a positive correlation between the percentage higher
than 20 % of women in the board and the Tobin’s Q ratio
Latest Results:
(-0.094388682158789469, -0.04962212239013655, -0.0068850052276448973)
Comments:
So no real causal influence here, wrong sign
'''
data, types = getData("spx_fboard","corp_gov_causal")
controlFor = stageAlgo(types)
treatment = 'X..Women.on.Bd'
target = 'Tobins.Q'
matcher = PropensityScoreMatching()
ATE_results = matcher.estimate_ATE(data, treatment, target, {'P.EBITDA': 'c', 'P.B': 'c', 'Asset':'c', 'Tax':'c', 'P.E':'c'}, bootstrap=True)
matcher.check_support(data, 'X..Women.on.Bd', {'P.EBITDA': 'c', 'P.B': 'c','Asset':'c', 'Tax':'c', 'P.E':'c'})
print("")
print("Balance before matching")
print(matcher.assess_balance(data, 'X..Women.on.Bd', {'P.EBITDA': 'c', 'P.B': 'c','Asset':'c', 'Tax':'c', 'P.E':'c', 'propensity score': 'c'}))
print ("")
data = matcher.score(data, assignment='X..Women.on.Bd', confounder_types={'P.EBITDA': 'c', 'P.B': 'c','Asset':'c', 'Tax':'c', 'P.E':'c'})
treated, control = matcher.match(data, assignment='X..Women.on.Bd')
print("")
print("Balance after matching")
print(matcher.assess_balance(treated.append(control), 'X..Women.on.Bd', {'P.EBITDA': 'c', 'P.B': 'c','Asset':'c', 'Tax':'c', 'P.E':'c', 'propensity score': 'c'}))
print ("")
#now write results to mysql
conn = MySQLdb.connect(host="localhost",
user="******",
passwd="",
db="causal_results")
cur = conn.cursor()
query = """ insert into akelleh_results values ('%s','%s','%s','%s','%s','%s'); """ % (now,"spx",treatment,target,str(ATE_results),"For the American companies inside the S and P 500 index, we found a positive correlation between the percentage higher than 20pct of women in the board and the Tobins Q ratio")
cur.execute(query)
conn.commit()
conn.close()
print("Done")
