Causalml

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)