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matheusfacure
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
The sentence reads "Second, learn what makes your work valuable and valuable," I guess we meant to say something like "reliable and valuable" or was this on purpose?
miss a right parenthesis in the equation below; the one at the front of Y_it doesn't one matched
I believe there is a typo in one of the formulas for the binary treatment target transformation, where $Y_i*(1-T_i) = Y(0)_i*T_i$ should be $Y_i*(1-T_i) = Y(0)_i*(1-T_i)$ Later in the demo,...
[Chapter 08]: Possible mistake in "Weakness of Instruments" example — instrument causality direction
Hi! I believe there may be a mistake in the example provided in the "Weakness of Instruments" section. The issue is that, in the code, the generated instrumental variables are...
The DGP in the example of the "What is Non-Parametric About" section assumes that the treatment is the discount and there's no other parameter. Can you explain a bit more...
Dear Matheus, I have a question about chapter 4, the last example (the mediator one), where you say conditioning on white collar biases the estimated effect of education on earnings....
Hi, I have a question on the **Sensitivity by Model Band** section of Chapter 19. Why do we compute the sensitivity for each band like this using a single variable...
I have a comment regarding the Y notation: while you precisely define the potential outcomes $Y_{0i}$ and $Y_{1i}$ the symbols $Y_{0}$ and $Y_{1}$ show up without exact definition. Later you...
There is an issue on chapter 7, in the following paragraph The first equation makes it explicit that we think those without the email and the agreement are better than...
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Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.