Add an explicit treatment input argument to causaltree/forest

[jeongyoonlee]

Proposed changes

This PR adds an explicit treatment input argument to CausalTreeRegressor.fit() and CausalRandomForestRegressor.fit().

Originally, it used sample_weight for treatment assignments to use the sklearn.tree APIs without modification, but, with #686, we don't use sklearn.tree anymore, and we're free to modify inputs to CausalTreeRegressor and CausalRandomForestRegressor.

All tests and notebook examples for causal tree/forest ran successfully without errors.

Types of changes

What types of changes does your code introduce to CausalML?
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• Bugfix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Documentation Update (if none of the other choices apply)

Checklist

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• I have read the CONTRIBUTING doc
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• Lint and unit tests pass locally with my changes
• I have added tests that prove my fix is effective or that my feature works
• I have added necessary documentation (if appropriate)
• Any dependent changes have been merged and published in downstream modules

Further comments

This change will make it easy to add multiple treatment support to causal tree/forest (#775).

[CLAassistant]

All committers have signed the CLA.

[ras44]

only general comment is to perhaps consider a "major version" bump to 0.16 once this is merged given that the API has changed and treatment is now a required arg

[jeongyoonlee]

@alexander-pv, can you take a look at this PR before I merge it? Thanks in advance!