Causallift

[2019/08/14]

CausalLift version 1.0.0 adopted [Kedro](https://kedro.readthedocs.io/) to add the following new
features.

- [Parallel execution] Train the 2 models in parallel
- [File management] Save and load intermediate files such as the trained models
- [Documentation] Generate the API document by Sphinx and visualize the process flow

Other enhancements include:

- [Logging] Show and/or log processing status such as timestamp and the running task
- [Model options] Specify models other than XGBoost and Logistic Regression for uplift
modeling and propensity modeling, respectively.

[2019/04/29]
Merged pull request [2](https://github.com/Minyus/causallift/pull/2), thanks farismosman !

- Add unit testing utility functions in the module named utils.
- Include a minor bug fix for the function overall_uplift_gain_ where col_treatment and col_outcome might not default to 'Treatment' and 'Outcome' respectively.
- Import missing numpy module.

[2019/04/29]
- Add simple test codes.
- Add "from IPython.display import display" so it can run in non-IPython environments.
- Fix "TypeError: object of type 'float' has no len()" that occurs if enable_ipw is set to False ([1](https://github.com/Minyus/causallift/issues/1), thanks farismosman !)
- [Examples] Skip installation of CausalLift if not run in Google Colab.

[2019/04/04]
Initial release