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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.
https://www.microsoft.com/en-us/research/project/alice/
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Difference between DeepIV and OrthoIV #674

Closed sami-ka closed 9 months ago

sami-ka commented 2 years ago

Hi, I would like to make sure that what I understood of the difference between OrthoIV and DeepIV is correct/complete.

Can we say that : DeepIV makes less restrictive assumptions on the DGP than OrthoIV. It is then more generic in that sense. However it involves the training of neural networks, which may be slower to converge to a good state compared to classic ML models. So : if we decide that the assumptions of the DGP behind OrthoIV is not too restrictive for our task, go with OrthoIV (easier to train) else go with DeepIV. ?

Feel free to correct me or complete the statement.

kbattocchi commented 2 years ago

I don't think I'd say the assumptions on the DGP are less restrictive for DeepIV, they're just different. The functional form of the relationship between Y and T can be arbitrary, but identifiability depends in a somewhat complex way on the distribution of T given X, W, and Z. For example, since we're trying find g minimizing (Y-∫g(T,X,W)dF(T|X,W,Z))², if the distribution of T is always symmetrical given X, W, and Z, then we can't identify the odd part of g, only the even part (since adding any odd function of T to g will give the same result for the integral).