帮我写一段Python代码，使用双重机器学习方法（随机森林回归和xgb回归）进行政策评估，数据集包含一个解释变量，一个被解释变量，四个中介机制和七个控制变量

好的，以下是一个使用双重机器学习方法（随机森林回归和XGBoost回归）进行政策评估的Python代码：

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

# 读取数据集
data = pd.read_csv('your_dataset.csv')

# 划分训练集和测试集
train, test = train_test_split(data, test_size=0.2, random_state=42)

# 定义解释变量、被解释变量、中介机制和控制变量
X = train[['explanatory_variable', 'mediator_1', 'mediator_2', 'mediator_3', 'mediator_4', 'control_variable_1', 'control_variable_2', 'control_variable_3', 'control_variable_4', 'control_variable_5', 'control_variable_6', 'control_variable_7']]
Z = train[['mediator_1', 'mediator_2', 'mediator_3', 'mediator_4']]
Y = train['dependent_variable']

# 随机森林回归模型
rf = RandomForestRegressor(n_estimators=100, random_state=42)
rf.fit(X, Z)

# 中介效应
Z_hat = rf.predict(X)
X_new = pd.concat([pd.DataFrame(X), pd.DataFrame(Z_hat, columns=['Z_hat_1', 'Z_hat_2', 'Z_hat_3', 'Z_hat_4'])], axis=1)
X_new = X_new.drop(['mediator_1', 'mediator_2', 'mediator_3', 'mediator_4'], axis=1)

# XGBoost回归模型
xgb = XGBRegressor(objective='reg:squarederror', random_state=42)
xgb.fit(X_new, Y)

# 测试集预测
X_test = test[['explanatory_variable', 'mediator_1', 'mediator_2', 'mediator_3', 'mediator_4', 'control_variable_1', 'control_variable_2', 'control_variable_3', 'control_variable_4', 'control_variable_5', 'control_variable_6', 'control_variable_7']]
Z_test = test[['mediator_1', 'mediator_2', 'mediator_3', 'mediator_4']]
Y_test = test['dependent_variable']
Z_hat_test = rf.predict(X_test)
X_new_test = pd.concat([pd.DataFrame(X_test), pd.DataFrame(Z_hat_test, columns=['Z_hat_1', 'Z_hat_2', 'Z_hat_3', 'Z_hat_4'])], axis=1)
X_new_test = X_new_test.drop(['mediator_1', 'mediator_2', 'mediator_3', 'mediator_4'], axis=1)
Y_pred = xgb.predict(X_new_test)

# 计算均方误差
mse = mean_squared_error(Y_test, Y_pred)
print('均方误差：', mse)

其中，需要将代码中的`your_dataset.csv`替换为你自己的数据集文件名，并根据实际情况修改解释变量、被解释变量、中介机制和控制变量的列名。