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")
import pandas as pd
import numpy as np
from causality.estimation.parametric import PropensityScoreMatching
N = 10000
z1 = np.random.normal(size=N)
z2 = np.random.normal(size=N)
z3 = np.random.normal(size=N)
p_d = 1. / (1. + np.exp(-(z1 + z2 + z3)/4.))
d = np.random.binomial(1, p=p_d)
y0 = np.random.normal()
y1 = y0 + z1 + z2 + z3
y = (d==1)*y1 + (d==0)*y0
X = pd.DataFrame({'d': d, 'z1': z1, 'z2': z2, 'z3': z3, 'y': y, 'y0': y0, 'y1': y1, 'p': p_d})
print(X.head(10))
matcher = PropensityScoreMatching()
print(matcher.estimate_ATE(X, 'd', 'y', {'z1': 'c', 'z2': 'c', 'z3': 'c'}))
matcher.check_support(X, 'd', {'z1': 'c', 'z2': 'c', 'z3': 'c'})
print(matcher.assess_balance(X, 'd', {'z1': 'c', 'z2': 'c', 'z3': 'c'}))
