Add examples for 'classic' causal inference datasets

[drbenvincent]

Suggestion by @juanitorduz... Rather than just applying the package to synthetic datasets, it would be good to apply the methods to classic datasets / causal inference problems. This also gives people some faith that the package produces sensible (or at least similar) results as other people's implementations.

Sources

• data from the book Mastering Metrics is available here http://www.masteringmetrics.com/resources/

RDD: drinking example

See https://matheusfacure.github.io/python-causality-handbook/16-Regression-Discontinuity-Design.html#

• [x] Frequentist model
• [x] Bayesian model
• [ ] Add reference/details of original study

SC: Proposition 99 example

• [ ] grab data
• [ ] Frequentist model
• [ ] Bayesian model

ITS: Add simple example to match the CausalImpact docs

• [ ] Generate similar data
• [ ] Add the example to its_pymc.ipynb
• [ ] Add the example to its_skl.ipynb

DiD: Add the 'bank failure' dataset + analyses

• [x] add data
• [x] add Bayesian analysis
  • [x] add case when we have multiple measurements over time (see #76)
• [ ] add Frequentist analysis

This will almost certainly require code changes. At the moment there is a hard wired constraint that there is just a single pre and post observation