Causal trees option to return counterfactual outcomes

[alexander-pv]

Proposed changes

Hi,

This feature adds an option for causal trees to return counterfactual outcomes $\hat{Y}(X|T=0)$, $\hat{Y}(X|T=1)$ along with estimated individual treatment effects.

Details:

• predict() method in CausalTreeRegressor and CausalRandomForestRegressor got flag with_outcomes that enables to return nx3 array of outcomes and treatment effects.
• Usage example was added to causal_trees_with_synthetic_data.ipynb notebook.
• New test for causal trees covers columns check in predict() output

Related issues: #590

Update:

• Dependencies fix: limit numpy version
• Requirements migration to pyproject.toml.

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[alexander-pv]

I updated package dependencies to make tests pass as expected and suggest moving requirements and metadata from setup.py to pyproject.toml as stated in pep-631 and pep-621

As a bonus, now you don't need to execute python setup.py build_ext --inplace to build cython modules before installing the package! Everything will be installed via pip install . However, it is still a requirement for pytest because test modules still import local causalml directory as a package.

[jeongyoonlee]

Thanks a lot! This will make installation and package management much easier. Really appreciate your contribution - as always!