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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 /19-Evaluating-Causal-Models.html #259

Closed pgmeiner closed 1 year ago

pgmeiner commented 2 years ago

I find the chapter about evaluating causal models very interesting and inspiring. I have not seen that before. However, sometimes I struggle with the terminology a bit. But maybe it is just my misunderstanding and you can help me here. When you talk about elasticity and estimate it with a linear regression aren't you actually estimating the partial slope (I also have sometimes the feeling you use elasticity and slope in exchange)? When elasticity between Y and T is defined as follows e = \partial Y / \partial T T/Y then the actual calculation would look like: e = \partial Y / \partial T T/Y = \partial (log(Y)) / \partial (log(T)) which would lead to log(Y) = log(beta_0) + e*log(T) So for estimating the elasticity e we would need to use log(Y) and log(T) instead of Y and T alone in the linear regression. If I misunderstood something please let me know. best regards Peter

matheusfacure commented 1 year ago

You are right. I've replaced elasticity with sensitivity to avoid the confusion.