Release v0.13.0 · uber/causalml

CausalML surpassed 1MM downloads on PyPI and 3,200 stars on GitHub. Thanks for choosing CausalML and supporting us on GitHub.
We have 7 new contributors @saiwing-yeung, @lixuan12315, @aldenrogers, @vincewu51, @AlkanSte, @enzoliao, and @alexander-pv. Thanks for your contributions!
@alexander-pv revamped CausalTreeRegressor and added CausalRandomForestRegressor with more seamless integration with scikit-learn's Cython tree module. He also added integration with shap for causal tree/ random forest interpretation. Please check out the example notebook.
We dropped the support for Python 3.6 and removed its test workflow.

What's Changed

Fix typo (% -> $) by @saiwing-yeung in #488
Add function for calculating PNS bounds by @t-tte in #482
Fix hard coding bug by @t-tte in #492
Update README of conda install and instruction of maintain in conda-forge by @ppstacy in #485
Update examples.rst by @lixuan12315 in #496
Fix incorrect effect_learner_objective in XGBRRegressor by @jeongyoonlee in #504
Fix Filter F doesn't work with latest statsmodels' F test f-value format by @paullo0106 in #505
Exclude tests in setup.py by @aldenrogers in #508
Enabling higher orders feature importance for F filter and LR filter by @zhenyuz0500 in #509
Ate pretrain 0506 by @vincewu51 in #511
Update methodology.rst by @AlkanSte in #518
Fix the bug of incorrect result in qini for multiple models by @enzoliao in #520
Test get_qini() by @enzoliao in #523
Fixed typo in uplift_trees_with_synthetic_data.ipynb by @jroessler in #531
Remove Python 3.6 test from workflows by @jeongyoonlee in #535
Causal trees update by @alexander-pv in #522
Causal trees interpretation example by @alexander-pv in #536

Full Changelog: v0.12.3...v0.13.0