Achieve parity in documentations and algorithms with EconML

[jeongyoonlee]

Recently the EconML team has made a good progress in their package. Let's achieve parity in common algorithms with EconML.

Documentation

• Add instruction for documentation to CONTRIBUTING.md - Update CONTRIBUTING.md #408 by @jeongyoonlee
• Add CausalML team's tutorials and publications in README.md - Update README.md - Adding talks and publication info #433 by @zhenyuz0500
• Update About CausalML in readthedocs - Update About section in docs #439 @t-tte
• Add documentation for neural-net based methods to Methodology Add documentation for neural-net based methods to Methodology #417 by @ppstacy
• Add documentation for IV methods to Methodology - Documentation Update on Instrument Variable Methods #447 by @huigangchen
• Update Installation - Update the installation doc in readthedocs #436 by @jeongyoonlee
• Add more examples and links to example notebooks to Examples - Add more examples and links to example notebooks  #445 by @paullo0106
• Reorg Interpretable CausalML, Validation, Visualization - Reorg the docs for the Interpretable Causal ML, Validation, Visualization #416 by @paullo0106
• Fix API documentation for causalml.inference.tree - Docs are not updated to date and miss some submodules #392 by @jeongyoonlee

Implementation

• CausalTree/CausalRandomForest with honest sampling
• Support sample_weights in R-learner - Add sample_weight support for R-learner  #425 by @paullo0106
• Add evaluation metrics for causal estimators. e.g. R-Scorer econml.score.RScorer

Please add if you find more.