Machine Learning, Causal Inference, Programming, Data Science.
μ1(x) = E[Y(1)|X=x] , we denote the two classifiers as \hat{μ0}. and \hat{μ1(x)}. Step 2: T-learner is \hat{τ_T(x)} = \hat{μ1(x)} — \hat{μ0(x)} https://arxiv.org/pdf/1706.03461.pdf 2. S-Learner “S-Learner” is similar to “T-Learner”, except that when we estimate the outcome, we use all predictors