def test_deepiv_models(self):
n = 2000
s1 = 2
s2 = 2
e = np.random.uniform(low=-0.5, high=0.5, size=(n, 1))
z = np.random.uniform(size=(n, 1))
x = np.random.uniform(size=(n, 1)) + e
p = x + z * e + np.random.uniform(size=(n, 1))
y = p * x + e
losses = []
marg_effs = []
z_fresh = np.random.uniform(size=(n, 1))
e_fresh = np.random.uniform(low=-0.5, high=0.5, size=(n, 1))
x_fresh = np.random.uniform(size=(n, 1)) + e_fresh
p_fresh = x_fresh + z_fresh * e_fresh + np.random.uniform(size=(n, 1))
y_fresh = p_fresh * x_fresh + e_fresh
for (n1, u, n2) in [(2, False, None), (2, True, None), (1, False, 1)]:
treatment_model = keras.Sequential([
keras.layers.Dense(10, activation='relu', input_shape=(2, )),
keras.layers.Dense(10, activation='relu'),
keras.layers.Dense(10, activation='relu')
])
hmodel = keras.Sequential([
keras.layers.Dense(10, activation='relu', input_shape=(2, )),
keras.layers.Dense(10, activation='relu'),
keras.layers.Dense(1)
])
deepIv = DeepIVEstimator(
10,
lambda z, x: treatment_model(keras.layers.concatenate([z, x])),
lambda t, x: hmodel(keras.layers.concatenate([t, x])),
n_samples=n1,
use_upper_bound_loss=u,
n_gradient_samples=n2,
s1=s1,
s2=s2)
deepIv.fit(y, p, x, z)
losses.append(
np.mean(np.square(y_fresh - deepIv.predict(p_fresh, x_fresh))))
marg_effs.append(
deepIv.marginal_effect(np.array([[0.3], [0.5], [0.7]]),
np.array([[0.4], [0.6], [0.2]])))
print("losses: {}".format(losses))
print("marg_effs: {}".format(marg_effs))
def test_deepiv_arbitrary_covariance(self):
d = 5
n = 5000
# to generate a random symmetric positive semidefinite covariance matrix, we can use A*A^T
A1 = np.random.normal(size=(d, d))
cov1 = np.matmul(A1, np.transpose(A1))
# convex combinations of semidefinite covariance matrices are themselves semidefinite
A2 = np.random.normal(size=(d, d))
cov2 = np.matmul(A2, np.transpose(A2))
m1 = np.random.normal(size=(d,))
m2 = np.random.normal(size=(d,))
x = np.random.uniform(size=(n, 1))
z = np.random.uniform(size=(n, 1))
alpha = (x * x + z * z) / 2 # in range [0,1]
t = np.array([np.random.multivariate_normal(m1 + alpha[i] * (m2 - m1),
cov1 + alpha[i] * (cov2 - cov1)) for i in range(n)])
y = np.expand_dims(np.einsum('nx,nx->n', t, t), -1) + x
results = []
s = 6
for (n1, u, n2) in [(2, False, None), (2, True, None), (1, False, 1)]:
treatment_model = keras.Sequential([keras.layers.Dense(90, activation='relu', input_shape=(2,)),
keras.layers.Dropout(0.2),
keras.layers.Dense(60, activation='relu'),
keras.layers.Dropout(0.2),
keras.layers.Dense(30, activation='relu')])
hmodel = keras.Sequential([keras.layers.Dense(90, activation='relu', input_shape=(d + 1,)),
keras.layers.Dropout(0.2),
keras.layers.Dense(60, activation='relu'),
keras.layers.Dropout(0.2),
keras.layers.Dense(30, activation='relu'),
keras.layers.Dropout(0.2),
keras.layers.Dense(1)])
deepIv = DeepIVEstimator(s,
lambda z, x: treatment_model(keras.layers.concatenate([z, x])),
lambda t, x: hmodel(keras.layers.concatenate([t, x])),
n_samples=n1, use_upper_bound_loss=u, n_gradient_samples=n2,
first_stage_options={'epochs': 20}, second_stage_options={'epochs': 20})
deepIv.fit(y[:n // 2], t[:n // 2], x[:n // 2], z[:n // 2])
results.append({'s': s, 'n1': n1, 'u': u, 'n2': n2,
'loss': np.mean(np.square(y[n // 2:] - deepIv.predict(t[n // 2:], x[n // 2:]))),
'marg': deepIv.marginal_effect(np.array([[0.5] * d]), np.array([[1.0]]))})
print(results)
