2. (Deep learning and CATE) Under conditional random assignment of a binary treatment D, recall that...

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2. (Deep learning and CATE) Under conditional random assignment of a binary treatment D, recall that CATE(W) E(Y|D=1,W) - E(Y|D=0, W). Discuss how to estimate CATE(W) using neural networks. (Hint: Recall that the conditional expectation is the function that achieves the smallest mean squared distance from Y.) 2. (Deep learning and CATE) Under conditional random assignment of a binary treatment D, recall that CATE(W) E(Y|D=1,W) - E(Y|D=0, W). Discuss how to estimate CATE(W) using neural networks. (Hint: Recall that the conditional expectation is the function that achieves the smallest mean squared distance from Y.)

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To estimate the Conditional Average Treatment Effect CATE using neural networks we can leverage the power of deep learning to approximate the conditio... View the full answer