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py-why / EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Home Page:https://www.microsoft.com/en-us/research/project/alice/

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Include moderators that are no confounders

FlorianNachtigall opened this issue · comments

commented

Hi!

Is it possible to include variables that only moderate but do not confound the relationship between T and Y?

Using the CausalForestDML, I would like to include an additional moderator to explain the heterogeneity of the treatment effect, but since it is also a mediator, I can't include it in X. I hope this makes sense.

Any thoughts and workarounds would be greatly appreciated. Thanks in advance!

commented

If you want to use it for heterogeneity you could still add it to X while changing your model_t to something like a pipeline where the first step is a ColumnTransformer that drops that column and the second step is your actual model.

However, if the variable is downstream of T, then such a CATE model will probably not be of much use in a policy setting, for example, because you can't make treatment decisions based on the unknown value of that variable at treatment time.