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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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Parameter tuning for Policy Learning

kayfoh opened this issue · comments

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I would like to start off by saying good job on coming up with the Econml package. It has definitely been a useful tool to my data science toolkit. I have one question about the policy learning models that I was hoping to get some clarification on.
For the policy learning models available in econml (DRPolicyForest, DRPolicyTree, etc), I noticed that there is no score method available. My question is, how am I able to determine the best parameters eg, max_depth, n_estimators in the case of PolicyForest, if there is no score for me to evaluate against? Is there another way to determine the right parameter values?