matheusfacure / python-causality-handbook

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
https://matheusfacure.github.io/python-causality-handbook/landing-page.html
MIT License
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Issue on page /22-Debiased-Orthogonal-Machine-Learning.html #352

Open 0scarrrrr opened 1 year ago

0scarrrrr commented 1 year ago

I am not sure that the example in What is Non-Parametric About? is correct

As you stay Sales_i = 20+10*Discounts^0.5 + ei

However this is linear in the parameters and not linear in the covariates

as delta_sales_i / delta_discounts_i = 5Discounts^(-0.5) - 5 being the parameter and Discounts being the covariate, therefore any estimator of the such Y = tau(Xi)T + ei should be able to learn that T = 5 and tau = Xi^-0.5

Am I missing something?