def test5():
"""The tests checks if the simulation process works even if the covariance between
U1 and V and U0 and V is equal. Further the test ensures that the mte_information
function returns the same value for each quantile.
"""
for _ in range(10):
generate_random_dict()
init_dict = read("test.grmpy.yml")
# We impose that the covariance between the random components of the potential
# outcomes and the random component determining choice is identical.
init_dict["DIST"]["params"][2] = init_dict["DIST"]["params"][4]
# Distribute information
coeffs_untreated = init_dict["UNTREATED"]["params"]
coeffs_treated = init_dict["TREATED"]["params"]
# Construct auxiliary information
cov = construct_covariance_matrix(init_dict)
df = simulate("test.grmpy.yml")
x = df[list(
set(init_dict["TREATED"]["order"] +
init_dict["UNTREATED"]["order"]))]
q = [0.01] + list(np.arange(0.05, 1, 0.05)) + [0.99]
mte = mte_information(coeffs_treated, coeffs_untreated, cov, q, x,
init_dict)
# We simply test that there is a single unique value for the marginal treatment
# effect.
np.testing.assert_equal(len(set(mte)), 1)
def test5():
"""The tests checks if the simulation process works even if the covariance between U1 and V
and U0 and V is equal. Further the test ensures that the mte_information function returns
the same value for each quantile.
"""
for _ in range(10):
generate_random_dict()
init_dict = read('test.grmpy.ini')
# We impose that the covariance between the random components of the potential
# outcomes and the random component determining choice is identical.
init_dict['DIST']['all'][2] = init_dict['DIST']['all'][4]
# Distribute information
coeffs_untreated = init_dict['UNTREATED']['all']
coeffs_treated = init_dict['TREATED']['all']
# Construct auxiliary information
cov = construct_covariance_matrix(init_dict)
df = simulate('test.grmpy.ini')
x = df.filter(regex=r'^X\_', axis=1)
q = [0.01] + list(np.arange(0.05, 1, 0.05)) + [0.99]
mte = mte_information(coeffs_treated, coeffs_untreated, cov, q, x)
# We simply test that there is a single unique value for the marginal treatment effect.
np.testing.assert_equal(len(set(mte)), 1)
def weights_treatment_parameters(init_dict, GRID):
"""This function calculates the weights for the special case in
Heckman & Vytlacil (2005) Figure 1B.
"""
GRID = np.linspace(0.01, 0.99, num=99, endpoint=True)
coeffs_untreated = init_dict['UNTREATED']['all']
coeffs_treated = init_dict['TREATED']['all']
cov = construct_covariance_matrix(init_dict)
x = simulate_covariates(init_dict)
x = x[:, :2]
# We take the specified distribution for the cost shifters from the paper.
cost_mean, cost_sd = -0.0026, np.sqrt(0.270)
v_mean, v_sd = 0.00, np.sqrt(cov[2, 2])
eval_points = norm.ppf(GRID, loc=v_mean, scale=v_sd)
ate_weights = np.tile(1.0, 99)
tut_weights = norm.cdf(eval_points, loc=cost_mean, scale=cost_sd)
tt_weights = 1 - tut_weights
def tut_integrand(point):
eval_point = norm.ppf(point, loc=v_mean, scale=v_sd)
return norm.cdf(eval_point, loc=cost_mean, scale=cost_sd)
def tt_integrand(point):
eval_point = norm.ppf(point, loc=v_mean, scale=v_sd)
return norm.cdf(eval_point, loc=cost_mean, scale=cost_sd)
# Scaling so that the weights integrate to one.
tut_scaling = quad(tut_integrand, 0.01, 0.99)[0]
tut_weights /= tut_scaling
tt_scaling = quad(tt_integrand, 0.01, 0.99)[0]
tt_weights /= tt_scaling
mte = mte_information(coeffs_treated, coeffs_untreated, cov, GRID, x,
init_dict)
return ate_weights, tt_weights, tut_weights, mte
def calculate_mte(rslt, init_dict, data_frame, quant=None):
coeffs_treated = rslt['TREATED']['all']
coeffs_untreated = rslt['UNTREATED']['all']
if quant is None:
quantiles = [1] + np.arange(2.5, 100, 2.5).tolist() + [99]
args = [str(i) + '%' for i in quantiles]
quantiles = [i * 0.01 for i in quantiles]
else:
quantiles = quant
cov = np.zeros((3, 3))
cov[2, 0] = rslt['AUX']['x_internal'][-3] * rslt['AUX']['x_internal'][-4]
cov[2, 1] = rslt['AUX']['x_internal'][-1] * rslt['AUX']['x_internal'][-2]
cov[2, 2] = 1.0
help_ = list(set(init_dict['TREATED']['order'] + init_dict['UNTREATED']['order']))
x = data_frame[[init_dict['varnames'][i - 1] for i in help_]]
value = mte_information(coeffs_treated, coeffs_untreated, cov, quantiles, x, rslt)
if quant is None:
return value, args
else:
return value
def calculate_mte(rslt, data_frame, quant=None):
coeffs_treated = rslt["TREATED"]["params"]
coeffs_untreated = rslt["UNTREATED"]["params"]
if quant is None:
quantiles = [1] + np.arange(5, 100, 5).tolist() + [99]
args = [str(i) + "%" for i in quantiles]
quantiles = [i * 0.01 for i in quantiles]
else:
quantiles = quant
cov = np.zeros((3, 3))
cov[2, 0] = rslt["AUX"]["x_internal"][-3] * rslt["AUX"]["x_internal"][-4]
cov[2, 1] = rslt["AUX"]["x_internal"][-1] * rslt["AUX"]["x_internal"][-2]
cov[2, 2] = 1.0
value = mte_information(coeffs_treated, coeffs_untreated, cov, quantiles,
data_frame, rslt)
if quant is None:
return value, args
else:
return value
index = GRID.index(round(xtick, 2))
height = mte[index]
ax.text(x=xtick - 0.002, y=height - 0.1, s='[', fontsize=30)
for xtick in [0.39, 0.79]:
index = GRID.index(round(xtick, 2))
height = mte[index]
ax.text(x=xtick - 0.01, y=height - 0.1, s=']', fontsize=30)
ax.set_xlabel('$u_S$')
ax.set_ylabel(r'$B^{MTE}$')
ax.set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1])
ax.set_xticklabels([0, '$p_1$', '$p_2$', '$p_3$', '$p_4$', 1])
ax.tick_params(axis='x', which='major')
ax.set_ylim([1.5, 4.5])
plt.tight_layout()
plt.savefig(ppj("OUT_FIGURES", 'fig-local-average-treatment.png'))
if __name__ == '__main__':
coeffs_untreated = init_dict['UNTREATED']['all']
coeffs_treated = init_dict['TREATED']['all']
cov = construct_covariance_matrix(init_dict)
x = np.loadtxt(ppj("OUT_DATA", "X.csv"), delimiter=",")
x = x.reshape(165, 2)
mte = mte_information(coeffs_treated, coeffs_untreated, cov, GRID, x)
plot_local_average_treatment(mte)
ax.plot(GRID, abs_, label="Absence", linestyle="--")
ax.set_ylim([1.5, 4.5])
plt.legend()
plt.tight_layout()
plt.savefig(OUTPUT_DIR + "/fig-eh-marginal-effect.png", dpi=300)
if __name__ == "__main__":
coeffs_untreated = init_dict["UNTREATED"]["params"]
coeffs_treated = init_dict["TREATED"]["params"]
cov = construct_covariance_matrix(init_dict)
df = simulate(RESOURCE_DIR + filename)
x = df[init_dict["TREATED"]["order"]]
MTE_pres = mte_information(coeffs_treated, coeffs_untreated, cov, GRID, x,
init_dict)
para_diff = coeffs_treated - coeffs_untreated
MTE_abs = []
for i in GRID:
if cov[2, 2] == 0.00:
MTE_abs += ["---"]
else:
MTE_abs += [np.mean(np.dot(para_diff, x.T))]
plot_marginal_treatment_effect(MTE_pres, MTE_abs)
cleanup()