def test_match(self):
matcher = PropensityScoreMatching()
X = pd.DataFrame({'assignment': [1, 0, 0, 0, 0, 0],
'propensity score': [3, 1, 2, 3, 5, 4]})
test, control = matcher.match(X, n_neighbors=3)
assert set(control['propensity score'].values) == set([2, 3, 4])
def test_match(self):
matcher = PropensityScoreMatching()
X = pd.DataFrame({
'assignment': [1, 0, 0, 0, 0, 0],
'propensity score': [3, 1, 2, 3, 5, 4]
})
test, control = matcher.match(X, n_neighbors=3)
assert set(control['propensity score'].values) == set([2, 3, 4])
def test_match(self):
matcher = PropensityScoreMatching()
X = pd.DataFrame(
{"assignment": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], "propensity score": [1, 2, 3, 4, 5, 1, 2, 3, 4, 5]}
)
test, control = matcher.match(X, n_neighbors=3)
matches = test[test["propensity score"] == 2]["matches"].values[0][0]
assert set(control.iloc[matches]["propensity score"].values) == set([1, 2, 3])
matches = test[test["propensity score"] == 4]["matches"].values[0][0]
assert set(control.iloc[matches]["propensity score"].values) == set([3, 4, 5])
def test_match(self):
matcher = PropensityScoreMatching()
X = pd.DataFrame({
'assignment': [1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
'propensity score': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5]
})
test, control = matcher.match(X, n_neighbors=3)
matches = test[test['propensity score'] == 2]['matches'].values[0][0]
assert set(control.iloc[matches]['propensity score'].values) == set(
[1, 2, 3])
matches = test[test['propensity score'] == 4]['matches'].values[0][0]
assert set(control.iloc[matches]['propensity score'].values) == set(
[3, 4, 5])
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")
