double ML support

[s3alfisc]

Should work for all doubleML classes, as they all inherit from the same base class doubleml.double_ml.DoubleML?

import numpy as np
from doubleml import DoubleMLData

from doubleml.datasets import fetch_bonus
from doubleml import DoubleMLPLR
from sklearn.base import clone
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LassoCV

np.random.seed(3141)

df_bonus = fetch_bonus('DataFrame')
n_obs = 500
n_vars = 100
theta = 3
X = np.random.normal(size=(n_obs, n_vars))
d = np.dot(X[:, :3], np.array([5, 5, 5])) + np.random.standard_normal(size=(n_obs,))
y = theta * d + np.dot(X[:, :3], np.array([5, 5, 5])) + np.random.standard_normal(size=(n_obs,))

dml_data_bonus = DoubleMLData(df_bonus,
                                 y_col='inuidur1',
                                 d_cols='tg',
                                 x_cols=['female', 'black', 'othrace', 'dep1', 'dep2',
                                         'q2', 'q3', 'q4', 'q5', 'q6', 'agelt35', 'agegt54',
                                         'durable', 'lusd', 'husd'])
 

dml_data_sim = DoubleMLData.from_arrays(X, y, d)
learner = RandomForestRegressor(n_estimators = 500, max_features = 'sqrt', max_depth= 5)
ml_l_bonus = clone(learner)
ml_m_bonus = clone(learner)
learner = LassoCV()
ml_l_sim = clone(learner)
ml_m_sim = clone(learner)
obj_dml_plr_bonus = DoubleMLPLR(dml_data_bonus, ml_l_bonus, ml_m_bonus)
obj_dml_plr_bonus.fit();
obj_dml_plr_sim = DoubleMLPLR(dml_data_sim, ml_l_sim, ml_m_sim)
obj_dml_plr_sim.fit();

import maketables as mt 

mt.ETable([obj_dml_plr_bonus, obj_dml_plr_sim], caption = 'doubleML treatment effect estimates')

produces