double roubst learning代码

Here is an example of double robust learning code using Python:

import numpy as np
from sklearn.linear_model import LinearRegression

# Generate some data
X = np.random.normal(0, 1, (100, 10))
beta_true = np.random.normal(0, 1, (10, 1))
Y = X.dot(beta_true) + np.random.normal(0, 1, (100, 1))

# Define two different models
model1 = LinearRegression()
model2 = LinearRegression()

# Fit the models using different methods
model1.fit(X[:, :5], Y)
model2.fit(X[:, 5:], Y)

# Combine the models using double robust learning
Y_pred = model1.predict(X[:, :5]) + model2.predict(X[:, 5:]) - model1.predict(X[:, 5:])
error = Y - Y_pred
model3 = LinearRegression()
model3.fit(X, error)
Y_pred_final = Y_pred + model3.predict(X)

# Print the results
print("True beta:", beta_true.flatten())
print("Estimated beta:", model3.coef_.flatten())

This code generates some random data and uses two different linear regression models to estimate the outcome. It then combines the predictions from the two models using double robust learning, and fits a final linear regression model to the residuals. The results are printed to the console, showing the true and estimated values of the beta coefficients.