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Preprocess compositional data #845
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The answer to this will depend somewhat on what specific models you use. If you use something like CausalForestDML (which will fit a non-parameterized final model) and you also use non-parametric models for your first stage model, then transforming X and W should generally not be necessary. However, the treatment effect model that we fit with any of our DML variants is always a linear effect of T on Y, so transforming Y and/or T will change the interpretation of the effect accordingly. (For example, in a pricing context you might take the log of Y and T, so that the computed effect is the price elasticity of demand, indicating that a one-percent change in price will have a corresponding percentage change in output, rather than that each price increase of a dollar will translate to a corresponding fixed decrease in demand). Some of our other models, like LinearDML, have more structure (e.g. assuming that the effect \theta(X) is linear in X), in which case you would want to consider whether a transforming your data beforehand might make more sense. Note that for convenience, most of our estimators allow you to pass a |
Thank you @kbattocchi ! I am using your |
That's correct - |
Hi EconML Team,
I am working on causal inference over compositional data. I wonder if we need to furhter preprocess our data first (e.g., Centered log ratio transform) before we use any causal inference model (e.g. DML).
Thank you.
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