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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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Get Treatment Decisions From Covariates #695

Open tykim6 opened 1 year ago

tykim6 commented 1 year ago

Is there a method for predicting optimal treatments based on an unseen dataset of covariates? Is there a valid way to choose an optimal treatment for an individual record?

If it makes a difference, I am using multiple treatments (3)

kbattocchi commented 1 year ago

Most of our estimators fit linear treatment effect models, in which case the treatment effect θ(X) (where X is the set of features) will indicate how strongly each treatment effects the outcome, so (for costless treatments) you'd optimally set each treatment with a positive coefficient as high as possible and each treatment will a negative coefficient as low as possible. In practice, you would want to apply domain knowledge to determine what range of treatments your existing data covers and how far you'd be comfortable extrapolating from that, since treatment effects are unlikely to really be linear for arbitrarily high values in general settings.

If you want to include non-linear effects, then you can featurize your treatment array to include higher-order terms (and our next release will make this much easier; for now you'd need to do it by hand), and you could also incorporate treatment costs by hand in devising your own strategy for optimizing treatment.