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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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Targeted Maximum Likelihood Estimator (TMLE) in EconML #725

Open AlxndrMlk opened 1 year ago

AlxndrMlk commented 1 year ago

Hi,

I was recently looking for Targeted Maximum Likelihood Estimator (TMLE) implementations in Python.

To my best understanding EconML does not implement this estimator. Is that correct?

If so, do you think it would be a good idea to add it to EconML?

Existing implementations:

[Perhaps there are more]

References:

kbattocchi commented 1 year ago

Thanks for the suggestion - for the case of a binary outcome this does seem like a potentially valuable addition to the library and it's something we'll consider.

AlxndrMlk commented 1 year ago

Thanks for the reply @kbattocchi

Is there a particular reason behind your suggestion regarding the binary outcome? Do you think that adding continuous outcomes would not be useful in the context of EconML?

Best, Alex