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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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Pytorch Support #39

Open jlevy44 opened 5 years ago

jlevy44 commented 5 years ago

Will pytorch support be offered for this package?

I can think of a number of biomedical applications that could largely benefit from these approaches.

kbattocchi commented 5 years ago

At the moment, many of our estimators build on top of any model that supports appropriate fit and predict methods, which could include custom models built on top of PyTorch. So, for example, you could pass a custom model class (where fit trains a PyTorch network and predict feeds the input forward through the trained network to get the result) as one of the arguments to the DMLCateEstimator initializer.

However, our DeepIVEstimator (which explicitly uses deep neural networks to perform instrumental variables estimation) is currently built on top of Keras with no corresponding way to use PyTorch. It would certainly be worthwhile to investigate whether we could include a PyTorch option as well, although it's not on our short-term roadmap. Thanks for the suggestion!