Causalml

* CausalML surpassed [1MM downloads](https://pepy.tech/project/causalml) on PyPI and [3,200 stars](https://github.com/uber/causalml/stargazers) 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](https://github.com/uber/causalml/blob/master/examples/causal_trees_interpretation.ipynb).
* We dropped the support for Python 3.6 and removed its test workflow.

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

New Contributors
* saiwing-yeung made their first contribution in https://github.com/uber/causalml/pull/488
* lixuan12315 made their first contribution in https://github.com/uber/causalml/pull/496
* aldenrogers made their first contribution in https://github.com/uber/causalml/pull/508
* vincewu51 made their first contribution in https://github.com/uber/causalml/pull/511
* AlkanSte made their first contribution in https://github.com/uber/causalml/pull/518
* enzoliao made their first contribution in https://github.com/uber/causalml/pull/520
* alexander-pv made their first contribution in https://github.com/uber/causalml/pull/522

**Full Changelog**: https://github.com/uber/causalml/compare/v0.12.3...v0.13.0

This patch is to release a version without the constraint of Shap which can be used for conda-forge.

What's Changed
* Modify the requirement version of Shap by ppstacy in https://github.com/uber/causalml/pull/483


**Full Changelog**: https://github.com/uber/causalml/compare/v0.12.2...v0.12.3

This patch includes three updates by our latest contributors, tonkolviktor and heiderich. We also start using [black](https://black.readthedocs.io/en/stable/integrations/index.html), a Python formatter. Please check out the updated [contribution guideline](https://github.com/uber/causalml/blob/master/CONTRIBUTING.md) to learn how to use it.

What's Changed
* Opens up scipy dependency version range towards newer releases (441) by tonkolviktor in https://github.com/uber/causalml/pull/473
* Merely define preferred backend for joblib instead of hard-coding it by heiderich in https://github.com/uber/causalml/pull/476
* Allow parallel prediction and make joblib's backend configurable for UpliftRandomForestClassifier by heiderich in https://github.com/uber/causalml/pull/477
* Reformat code using black by jeongyoonlee in https://github.com/uber/causalml/pull/474

New Contributors
* tonkolviktor made their first contribution in https://github.com/uber/causalml/pull/473
* heiderich made their first contribution in https://github.com/uber/causalml/pull/476

**Full Changelog**: https://github.com/uber/causalml/compare/v0.12.1...v0.12.2

This patch includes two bug fixes for UpliftRandomForestClassifier as follows:

- 462 by paullo0106: Use the correct `treatment_idx` for `fillTree()` when applying validation data set
- 468 by jeongyoonlee: Switch the joblib backend for UpliftRandomForestClassifier to threading to avoid memory copy across trees

- CausalML surpassed [637K downloads](https://pepy.tech/project/causalml) on PyPI and [2,500 stars](https://github.com/uber/causalml/stargazers) on Github!
- We have 4 new community contributors, Luis (lgmoneda ), Ravi (raviksharma), Louis (LouisHernandez17) and JackRab (JackRab). Thanks for the contribution!
- We refactored and speeded up UpliftTreeClassifier/UpliftRandomForestClassifier by 5x with Cython (422 440 by jeongyoonlee)
- We revamped our [API documentation](https://causalml.readthedocs.io/en/latest/about.html), it now includes the latest methodology, references, installation, notebook examples, and graphs! (#413 by huigangchen t-tte zhenyuz0500 jeongyoonlee paullo0106)
- Our team gave talks at [2021 Conference on Digital Experimentation MIT (CODEMIT)](https://ide.mit.edu/events/2021-conference-on-digital-experimentation-mit-codemit/), [Causal Data Science Meeting 2021](https://www.causalscience.org/meeting/program/day-2/), and [KDD 2021 Tutorials](https://causal-machine-learning.github.io/kdd2021-tutorial/) on CausalML introduction and applications. Please take a look if you missed them! Full list of publications and talks can be found [here](https://github.com/uber/causalml#conference-talks-and-publications-by-causalml-team).

Updates

- Update documentation on Instrument Variable methods huigangchen (447)
- Add benchmark simulation studies example notebook by t-tte (443)
- Add sample_weight support for R-learner by paullo0106 (425)
- Fix incorrect binning of numeric features in UpliftTreeClassifier by jeongyoonlee (420)
- Update papers, talks, and publication info to README and refs.bib by zhenyuz0500 (410 414 433)
- Add instruction for contributing.md doc by jeongyoonlee (408)
- Fix incorrect feature importance calculation logic by paullo0106 (406)
- Add parallel jobs support for NearestNeighbors search with n_jobs parameter by paullo0106 (389)
- Fix bug in simulate_randomized_trial by jroessler (385)
- Add GA pytest workflow by ppstacy (380)