Refactor uplift trees by replacing dict with list

[jeongyoonlee]

Proposed changes

This PR refactors UpliftTreeClassifier and UpliftRandomForestClassifier in inference.tree.

Updates include:

• Replacing dict with list for optimization with numba or cython (Speed up UpliftRandomForestClassifier? #54)
• Updating docstrings to reflect the correct input and return types (Maybe wrong return obj in UpliftRandomForestClassifier.predict() #227)
• Using np.random.RandomState in UpliftTreeClassifier and UpliftRandomForestClassifier (Add random_state to upliftTreeClassifier and upliftRandomForestClassifier #401)
• Updating UpliftRandomForestClassifier.classes_ to include control_name as the first item
• Moving generic functions (kl_divergence, entropyH) out of the UpliftTreeClassifier class to inference.tree.utils
• Removing unused/unnecessary input/return arguments
• Updating tests and example notebooks accordingly

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

Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your code.

• I have read the CONTRIBUTING doc
• I have signed the CLA
• 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

No change in public APIs except UpliftRandomForest.classes_, which now includes control_name as the first item.

[paullo0106]

Looks really nice and helpful!

[paullo0106]

LGTM, thanks!