def generate_outcomes(self, x, a, z):
y0_prob = np.expand_dims(
sigmoid(np.mean(x, axis=1) / self.outcome_scale), 1)
y1_prob = np.expand_dims(
sigmoid(-np.mean(x, axis=1) / self.outcome_scale), 1)
y0 = np.random.binomial(1, y0_prob)
y1 = np.random.binomial(1, y1_prob)
return y0, y1, y0_prob, y1_prob
def generate_outcomes(self, x, a, z):
mn = np.mean(x, axis=1)
logit_below_0 = mn / self.outcome_scale
logit_above_0 = (mn + self.offset) / self.outcome_scale
logits = switch(logit_below_0, logit_above_0, mn > 0)
y0_prob = np.expand_dims(sigmoid(logits), 1)
y1_prob = np.expand_dims(sigmoid(-logits), 1)
y0 = np.random.binomial(1, y0_prob)
y1 = np.random.binomial(1, y1_prob)
return y0, y1, y0_prob, y1_prob
def choose_observed_treatment(self, x, a, z):
# A = 0 treatment
a0_t_obs_prob = np.expand_dims(
sigmoid(np.mean(x + z, axis=1) / self.outcome_scale), 1)
a0_t_obs = np.random.binomial(1, a0_t_obs_prob)
a0_t_obs_cf = 1 - a0_t_obs
# A = 1 treatment
a1_t_obs_prob = np.expand_dims(
sigmoid(np.mean(x - z, axis=1) / self.outcome_scale), 1)
a1_t_obs = np.random.binomial(1, a1_t_obs_prob)
a1_t_obs_cf = 1 - a1_t_obs
#combine outcomes by A
t_obs = switch(a0_t_obs, a1_t_obs, a)
t_obs_prob = switch(a0_t_obs_prob, a1_t_obs_prob, a)
t_obs_cf = switch(a0_t_obs_cf, a1_t_obs_cf, a)
return t_obs, t_obs_cf, t_obs_prob
def generate_outcomes(self, x, a, z):
#A = 0 outcomes
a0_y0_prob = np.expand_dims(
sigmoid(np.mean(x + z, axis=1) / self.outcome_scale), 1)
a0_y1_prob = np.expand_dims(
sigmoid(-np.mean(x - z, axis=1) / self.outcome_scale), 1)
a0_y0 = np.random.binomial(1, a0_y0_prob)
a0_y1 = np.random.binomial(1, a0_y1_prob)
#A = 1 outcomes
a1_y0_prob = np.expand_dims(
sigmoid(np.mean(x - z, axis=1) / self.outcome_scale), 1)
a1_y1_prob = np.expand_dims(
sigmoid(-np.mean(x + z, axis=1) / self.outcome_scale), 1)
a1_y0 = np.random.binomial(1, a1_y0_prob)
a1_y1 = np.random.binomial(1, a1_y1_prob)
#combine outcomes by A
y0 = switch(a0_y0, a1_y0, a)
y1 = switch(a0_y1, a1_y1, a)
y0_prob = switch(a0_y0_prob, a1_y0_prob, a)
y1_prob = switch(a0_y1_prob, a1_y1_prob, a)
return y0, y1, y0_prob, y1_prob
def generate_outcomes(x):
y0_prob = np.expand_dims(sigmoid(np.sum(x, axis=1) / 5.), 1)
y1_prob = np.expand_dims(sigmoid(-np.sum(x, axis=1) / 5.), 1)
y0 = np.random.binomial(1, y0_prob)
y1 = np.random.binomial(1, y1_prob)
return y0, y1, y0_prob, y1_prob
def choose_observed_treatment(x):
t_obs_prob = np.expand_dims(sigmoid(np.sum(x, axis=1) / 5.), 1)
t_obs = np.random.binomial(1, t_obs_prob)
t_obs_cf = 1 - t_obs
return t_obs, t_obs_cf, t_obs_prob
def choose_observed_treatment(self, x, a, z):
t_obs_prob = np.expand_dims(
sigmoid(np.mean(x, axis=1) / self.outcome_scale), 1)
t_obs = np.random.binomial(1, t_obs_prob)
t_obs_cf = 1 - t_obs
return t_obs, t_obs_cf, t_obs_prob