因果推断笔记——因果图建模之Uber开源的CausalML(十二)3.2 meta元学习估计ATE/ITE
3.2.1 『ATE』S-Learning 单模型 - 线性模型
3.2.2 『ATE』双模型T-Learner + XGB回归
3.2.3 『ATE』Base (ate_s[0][0]))
print('ATE lower bound: {:.03f}'.format(ate_s[1][0]))
print('ATE upper bound: {:.03f}' .format(ate_s[2][0]))
输出ATE:
(array([0.68844541]), array([0.64017928]), array([0.73671154]))
ATE estimate : 0.688
ATE lower bound: 0.640
ATE upper bound: 0.737
3.2.2 『ATE』双模型T-Learner + XGB回归
# Ready-to-use , t_ate, x_ate, r_ate, dragon_ate, tau.mean()],
index=['S','T','X','R','dragonnet 