Multivariate Treatment Causal Inference¶

Load necessary libraries

In [1]:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split

from stochtree import BCFModel

import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns from sklearn.model_selection import train_test_split from stochtree import BCFModel

Generate sample data

In [2]:

# RNG
rng = np.random.default_rng()

# Generate covariates and basis
n = 500
p_X = 5
X = rng.uniform(0, 1, (n, p_X))
pi_X = np.c_[0.25 + 0.5 * X[:, 0], 0.75 - 0.5 * X[:, 1]]
# Z = rng.uniform(0, 1, (n, 2))
Z = rng.binomial(1, pi_X, (n, 2))

# Define the outcome mean functions (prognostic and treatment effects)
mu_X = pi_X[:, 0] * 5 + pi_X[:, 1] * 2 + 2 * X[:, 2]
tau_X = np.stack((X[:, 1], X[:, 2]), axis=-1)

# Generate outcome
epsilon = rng.normal(0, 1, n)
treatment_term = np.multiply(tau_X, Z).sum(axis=1)
y = mu_X + treatment_term + epsilon

# RNG rng = np.random.default_rng() # Generate covariates and basis n = 500 p_X = 5 X = rng.uniform(0, 1, (n, p_X)) pi_X = np.c_[0.25 + 0.5 * X[:, 0], 0.75 - 0.5 * X[:, 1]] # Z = rng.uniform(0, 1, (n, 2)) Z = rng.binomial(1, pi_X, (n, 2)) # Define the outcome mean functions (prognostic and treatment effects) mu_X = pi_X[:, 0] * 5 + pi_X[:, 1] * 2 + 2 * X[:, 2] tau_X = np.stack((X[:, 1], X[:, 2]), axis=-1) # Generate outcome epsilon = rng.normal(0, 1, n) treatment_term = np.multiply(tau_X, Z).sum(axis=1) y = mu_X + treatment_term + epsilon

Test-train split

In [3]:

sample_inds = np.arange(n)
train_inds, test_inds = train_test_split(sample_inds, test_size=0.5)
X_train = X[train_inds, :]
X_test = X[test_inds, :]
Z_train = Z[train_inds, :]
Z_test = Z[test_inds, :]
y_train = y[train_inds]
y_test = y[test_inds]
pi_train = pi_X[train_inds]
pi_test = pi_X[test_inds]
mu_train = mu_X[train_inds]
mu_test = mu_X[test_inds]
tau_train = tau_X[train_inds, :]
tau_test = tau_X[test_inds, :]

sample_inds = np.arange(n) train_inds, test_inds = train_test_split(sample_inds, test_size=0.5) X_train = X[train_inds, :] X_test = X[test_inds, :] Z_train = Z[train_inds, :] Z_test = Z[test_inds, :] y_train = y[train_inds] y_test = y[test_inds] pi_train = pi_X[train_inds] pi_test = pi_X[test_inds] mu_train = mu_X[train_inds] mu_test = mu_X[test_inds] tau_train = tau_X[train_inds, :] tau_test = tau_X[test_inds, :]

Run BCF

In [4]:

bcf_model = BCFModel()
bcf_model.sample(
    X_train=X_train,
    Z_train=Z_train,
    y_train=y_train,
    pi_train=pi_train,
    X_test=X_test,
    Z_test=Z_test,
    pi_test=pi_test,
    num_gfr=10,
    num_mcmc=100,
)

bcf_model = BCFModel() bcf_model.sample( X_train=X_train, Z_train=Z_train, y_train=y_train, pi_train=pi_train, X_test=X_test, Z_test=Z_test, pi_test=pi_test, num_gfr=10, num_mcmc=100, )

Inspect the MCMC (BART) samples

In [5]:

forest_preds_y_mcmc = bcf_model.y_hat_test
y_avg_mcmc = np.squeeze(forest_preds_y_mcmc).mean(axis=1, keepdims=True)
y_df_mcmc = pd.DataFrame(
    np.concatenate((np.expand_dims(y_test, 1), y_avg_mcmc), axis=1),
    columns=["True outcome", "Average estimated outcome"],
)
sns.scatterplot(data=y_df_mcmc, x="Average estimated outcome", y="True outcome")
plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3, 3)))
plt.show()

forest_preds_y_mcmc = bcf_model.y_hat_test y_avg_mcmc = np.squeeze(forest_preds_y_mcmc).mean(axis=1, keepdims=True) y_df_mcmc = pd.DataFrame( np.concatenate((np.expand_dims(y_test, 1), y_avg_mcmc), axis=1), columns=["True outcome", "Average estimated outcome"], ) sns.scatterplot(data=y_df_mcmc, x="Average estimated outcome", y="True outcome") plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3, 3))) plt.show()

In [6]:

np.sqrt(np.mean(np.power(y_avg_mcmc - y_test, 2)))

np.sqrt(np.mean(np.power(y_avg_mcmc - y_test, 2)))

Out[6]:

np.float64(1.913979587642076)

In [7]:

treatment_idx = 0
forest_preds_tau_mcmc = np.squeeze(bcf_model.tau_hat_test[:, :, treatment_idx])
tau_avg_mcmc = np.squeeze(forest_preds_tau_mcmc).mean(axis=1, keepdims=True)
tau_df_mcmc = pd.DataFrame(
    np.concatenate(
        (np.expand_dims(tau_test[:, treatment_idx], 1), tau_avg_mcmc), axis=1
    ),
    columns=["True tau", "Average estimated tau"],
)
sns.scatterplot(data=tau_df_mcmc, x="True tau", y="Average estimated tau")
plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3, 3)))
plt.show()

treatment_idx = 0 forest_preds_tau_mcmc = np.squeeze(bcf_model.tau_hat_test[:, :, treatment_idx]) tau_avg_mcmc = np.squeeze(forest_preds_tau_mcmc).mean(axis=1, keepdims=True) tau_df_mcmc = pd.DataFrame( np.concatenate( (np.expand_dims(tau_test[:, treatment_idx], 1), tau_avg_mcmc), axis=1 ), columns=["True tau", "Average estimated tau"], ) sns.scatterplot(data=tau_df_mcmc, x="True tau", y="Average estimated tau") plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3, 3))) plt.show()

In [8]:

treatment_idx = 1
forest_preds_tau_mcmc = np.squeeze(bcf_model.tau_hat_test[:, :, treatment_idx])
tau_avg_mcmc = np.squeeze(forest_preds_tau_mcmc).mean(axis=1, keepdims=True)
tau_df_mcmc = pd.DataFrame(
    np.concatenate(
        (np.expand_dims(tau_test[:, treatment_idx], 1), tau_avg_mcmc), axis=1
    ),
    columns=["True tau", "Average estimated tau"],
)
sns.scatterplot(data=tau_df_mcmc, x="True tau", y="Average estimated tau")
plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3, 3)))
plt.show()

treatment_idx = 1 forest_preds_tau_mcmc = np.squeeze(bcf_model.tau_hat_test[:, :, treatment_idx]) tau_avg_mcmc = np.squeeze(forest_preds_tau_mcmc).mean(axis=1, keepdims=True) tau_df_mcmc = pd.DataFrame( np.concatenate( (np.expand_dims(tau_test[:, treatment_idx], 1), tau_avg_mcmc), axis=1 ), columns=["True tau", "Average estimated tau"], ) sns.scatterplot(data=tau_df_mcmc, x="True tau", y="Average estimated tau") plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3, 3))) plt.show()

In [9]:

treatment_term_mcmc_test = np.multiply(
    np.atleast_3d(Z_test).swapaxes(1, 2), bcf_model.tau_hat_test
).sum(axis=2)
treatment_term_test = np.multiply(tau_test, Z_test).sum(axis=1)
treatment_term_mcmc_avg = np.squeeze(treatment_term_mcmc_test).mean(
    axis=1, keepdims=True
)
mu_df_mcmc = pd.DataFrame(
    np.concatenate(
        (np.expand_dims(treatment_term_test, 1), treatment_term_mcmc_avg), axis=1
    ),
    columns=["True treatment term", "Average estimated treatment term"],
)
sns.scatterplot(
    data=mu_df_mcmc, x="True treatment term", y="Average estimated treatment term"
)
plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3, 3)))
plt.show()

treatment_term_mcmc_test = np.multiply( np.atleast_3d(Z_test).swapaxes(1, 2), bcf_model.tau_hat_test ).sum(axis=2) treatment_term_test = np.multiply(tau_test, Z_test).sum(axis=1) treatment_term_mcmc_avg = np.squeeze(treatment_term_mcmc_test).mean( axis=1, keepdims=True ) mu_df_mcmc = pd.DataFrame( np.concatenate( (np.expand_dims(treatment_term_test, 1), treatment_term_mcmc_avg), axis=1 ), columns=["True treatment term", "Average estimated treatment term"], ) sns.scatterplot( data=mu_df_mcmc, x="True treatment term", y="Average estimated treatment term" ) plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3, 3))) plt.show()

In [10]:

forest_preds_mu_mcmc = bcf_model.mu_hat_test
mu_avg_mcmc = np.squeeze(forest_preds_mu_mcmc).mean(axis=1, keepdims=True)
mu_df_mcmc = pd.DataFrame(
    np.concatenate((np.expand_dims(mu_test, 1), mu_avg_mcmc), axis=1),
    columns=["True mu", "Average estimated mu"],
)
sns.scatterplot(data=mu_df_mcmc, x="True mu", y="Average estimated mu")
plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3, 3)))
plt.show()

forest_preds_mu_mcmc = bcf_model.mu_hat_test mu_avg_mcmc = np.squeeze(forest_preds_mu_mcmc).mean(axis=1, keepdims=True) mu_df_mcmc = pd.DataFrame( np.concatenate((np.expand_dims(mu_test, 1), mu_avg_mcmc), axis=1), columns=["True mu", "Average estimated mu"], ) sns.scatterplot(data=mu_df_mcmc, x="True mu", y="Average estimated mu") plt.axline((0, 0), slope=1, color="black", linestyle=(0, (3, 3))) plt.show()

In [11]:

sigma_df_mcmc = pd.DataFrame(
    np.concatenate(
        (
            np.expand_dims(
                np.arange(bcf_model.num_samples - bcf_model.num_gfr), axis=1
            ),
            np.expand_dims(bcf_model.global_var_samples[bcf_model.num_gfr :], axis=1),
        ),
        axis=1,
    ),
    columns=["Sample", "Sigma"],
)
sns.scatterplot(data=sigma_df_mcmc, x="Sample", y="Sigma")
plt.show()

sigma_df_mcmc = pd.DataFrame( np.concatenate( ( np.expand_dims( np.arange(bcf_model.num_samples - bcf_model.num_gfr), axis=1 ), np.expand_dims(bcf_model.global_var_samples[bcf_model.num_gfr :], axis=1), ), axis=1, ), columns=["Sample", "Sigma"], ) sns.scatterplot(data=sigma_df_mcmc, x="Sample", y="Sigma") plt.show()