Comments (1)
Hi! Thank you for your comment.
However, I couldn't transform back ATT from its value estimated from log input data to one unscaled.
As a simple example, consider a linear model. Think about the meaning of the following x1 coefficient when the outcome variable Y is log-transformed.
As you can see from this
β1 means "Y increases by β1( = log Y'- log Y) when x1 increases by 1".
Since log(1 + y) can be approximated as y, the following transformation is possible:
log Y'- log Y = log(Y'/Y)
= log(1 + (Y' - Y)/Y )
~ (Y' - Y) / Y
In other words, we can think of it as "Y increases by β% as x1 increases by 1.
This is the same in this model, which can be interpreted as an ATT of {tau}%!
I apologize if I misread the intent of your question.
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