This is a major release, with the following important changes:

• Richer inference support, including hypothesis testing, p-values, and more when using linear models (#203)
• New estimators supporting orthogonal approaches to IV, including DML IV and DR IV (#218)
• Experimental support for using Azure Automated Machine Learning for model selection (#213)
• Allows the use of bootstrap of little bags for inference with the OrthoForest (#214)
• The CATE policy interpreter can now assign treatments to new units based on the learned policy (#228)
• Added new Jupyter notebooks illustrating how to use the library for end-to-end scenarios (#230)
• Several minor bugfixes (#220, #212, #223, #225, #226, #227, #232)